{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'module://ipykernel.pylab.backend_inline'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib\n",
    "matplotlib.get_backend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install ibats_common\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "from sklearn import ensemble, preprocessing, metrics\n",
    "from ibats_common.backend.factor import get_factor\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from datetime import datetime\n",
    "from vnpy.trader.constant import Exchange, Interval\n",
    "from vnpy.trader.database import database_manager\n",
    "import ffn  # NOQA\n",
    "import matplotlib\n",
    "# 使用 Jupyter 内置方式显示\n",
    "matplotlib.use('module://ipykernel.pylab.backend_inline')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def plot_bar(symbol=\"RB2010\", exchange=Exchange.SHFE, interval=Interval.MINUTE,\n",
    "             start=datetime(2019, 4, 1), end=datetime(2020, 10, 30),\n",
    "             label_count=15, fig_size=(16, 6), label_rotation=15, time_format='%Y-%m-%d %H:%M:%S'):\n",
    "    # Load history data\n",
    "    bars = database_manager.load_bar_data(\n",
    "        symbol=symbol, exchange=exchange,\n",
    "        interval=interval, start=start, end=end)\n",
    "\n",
    "    # Generate x, y\n",
    "    x = [bar.datetime for bar in bars]\n",
    "    y = [bar.close_price for bar in bars]\n",
    "\n",
    "    # Show plot\n",
    "    y_len = len(y)\n",
    "    xticks = list(range(0, y_len, y_len // label_count))\n",
    "    xlabels = [x[_].strftime(time_format) for _ in xticks]\n",
    "    fig, ax = plt.subplots(figsize=fig_size)\n",
    "    ax.set_xticks(xticks)\n",
    "    ax.set_xticklabels(xlabels, rotation=label_rotation)\n",
    "    plt.plot(y)\n",
    "    plt.title(f\"{symbol} {interval.value} {min(x).strftime(time_format)}~{max(x).strftime(time_format)}\")\n",
    "    plt.legend([symbol])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-21 11:32:33,885 DEBUG peewee peewee.py.execute_sql:3087|('SELECT `t1`.`id`, `t1`.`symbol`, `t1`.`exchange`, `t1`.`datetime`, `t1`.`interval`, `t1`.`volume`, `t1`.`open_interest`, `t1`.`open_price`, `t1`.`high_price`, `t1`.`low_price`, `t1`.`close_price` FROM `dbbardata` AS `t1` WHERE (((((`t1`.`symbol` = %s) AND (`t1`.`exchange` = %s)) AND (`t1`.`interval` = %s)) AND (`t1`.`datetime` >= %s)) AND (`t1`.`datetime` <= %s)) ORDER BY `t1`.`datetime`', ['RB2009', 'SHFE', '1m', datetime.datetime(2019, 4, 1, 0, 0), datetime.datetime(2020, 10, 30, 0, 0)])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_bar(symbol=\"RB2009\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ibats_common.backend.factor import get_factor\n",
    "\n",
    "BAR_ATTRIBUTES = [\n",
    "    'open_price', 'high_price', 'low_price', 'close_price',\n",
    "    'datetime', 'volume',\n",
    "]\n",
    "\n",
    "def generate_factors(hist_bar_list):\n",
    "    \"\"\"整理缓存数据，生成相应的因子\"\"\"\n",
    "    df = pd.DataFrame(\n",
    "        [{key: getattr(_, key) for key in BAR_ATTRIBUTES}\n",
    "         for _ in hist_bar_list]).set_index('datetime')\n",
    "    df.index = pd.to_datetime(df.index)\n",
    "\n",
    "    # 生成因子\n",
    "    factor_df = get_factor(\n",
    "        df,\n",
    "        ohlcav_col_name_list=['open_price', 'high_price', 'low_price', 'close_price', None, 'volume'],\n",
    "        dropna=False\n",
    "    )\n",
    "    return df, factor_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-09-21 11:32:35,911 DEBUG peewee peewee.py.execute_sql:3087|('SELECT `t1`.`id`, `t1`.`symbol`, `t1`.`exchange`, `t1`.`datetime`, `t1`.`interval`, `t1`.`volume`, `t1`.`open_interest`, `t1`.`open_price`, `t1`.`high_price`, `t1`.`low_price`, `t1`.`close_price` FROM `dbbardata` AS `t1` WHERE (((((`t1`.`symbol` = %s) AND (`t1`.`exchange` = %s)) AND (`t1`.`interval` = %s)) AND (`t1`.`datetime` >= %s)) AND (`t1`.`datetime` <= %s)) ORDER BY `t1`.`datetime`', ['RB2009', 'SHFE', '1m', datetime.datetime(2019, 4, 1, 0, 0), datetime.datetime(2020, 10, 30, 0, 0)])\n"
     ]
    }
   ],
   "source": [
    "symbol=\"RB2009\"\n",
    "exchange=Exchange.SHFE\n",
    "interval=Interval.MINUTE\n",
    "start=datetime(2019, 4, 1)\n",
    "end=datetime(2020, 10, 30)\n",
    "bars = database_manager.load_bar_data(\n",
    "    symbol=symbol, exchange=exchange,\n",
    "    interval=interval, start=start, end=end)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(BarData(gateway_name='DB', symbol='RB2009', exchange=<Exchange.SHFE: 'SHFE'>, datetime=datetime.datetime(2020, 1, 2, 9, 0, tzinfo=<DstTzInfo 'Asia/Shanghai' LMT+8:06:00 STD>), interval=<Interval.MINUTE: '1m'>, volume=3.0, open_interest=2149.0, open_price=3488.0, high_price=3488.0, low_price=3488.0, close_price=3488.0),\n",
       " datetime.datetime(2020, 1, 2, 9, 0, tzinfo=<DstTzInfo 'Asia/Shanghai' LMT+8:06:00 STD>),\n",
       " datetime.datetime(2020, 6, 15, 15, 0, tzinfo=<DstTzInfo 'Asia/Shanghai' LMT+8:06:00 STD>))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bars[0], bars[0].datetime, bars[-1].datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建因子矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29047, (29047, 5), (29047, 111))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_bar_df, factor_df = generate_factors(bars)\n",
    "len(bars), hist_bar_df.shape, factor_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hist_bar_df['close_price'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "构建y数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13387, 11565)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_n_bars = 5\n",
    "y_s = hist_bar_df['close_price'].rolling(\n",
    "    window=target_n_bars).apply(lambda x: x.calc_calmar_ratio())  # calc_total_return calc_calmar_ratio\n",
    "y_s[np.isneginf(y_s)]=-100\n",
    "y_s[np.isposinf(y_s)]=100\n",
    "np.sum(y_s<=0), np.sum(y_s>0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据整理，剔除无效数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((24868, 111), (24868,), 13328, 11540)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 剔除无效数据，并根据 target_n_bars 进行数据切片\n",
    "is_available = ~(np.isinf(y_s) | np.isnan(y_s) | np.any(np.isnan(factor_df), axis=1))\n",
    "available_hist_bar_df = hist_bar_df[is_available].iloc[:-target_n_bars]\n",
    "available_factor_df = factor_df[is_available].iloc[:-target_n_bars]\n",
    "x_arr = available_factor_df.to_numpy()\n",
    "y_arr = y_s[is_available][target_n_bars:]\n",
    "assert x_arr.shape[0] == y_arr.shape[0], \"因子数据 x 长度要与训练目标 y 数据长度一致\"\n",
    "\n",
    "# 生成 -1 1 分类结果\n",
    "y_arr[y_arr > 0] = 1\n",
    "y_arr[y_arr <= 0] = -1\n",
    "x_train_arr, x_test_arr, y_train_arr, y_test_arr = train_test_split(\n",
    "    x_arr, y_arr, test_size=0.3)\n",
    "x_arr.shape, y_arr.shape, np.sum(y_arr==-1), np.sum(y_arr==1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "创建分类器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train classifier\n",
    "# 弱学习器的最大迭代次数，\n",
    "# n_estimators太小，容易欠拟合，n_estimators太大，又容易过拟合，\n",
    "# 一般选择一个适中的数值。默认是50。\n",
    "# 在实际调参的过程中，我们常常将n_estimators和learning_rate一起考虑。\n",
    "n_estimators = 50\n",
    "# 对于同样的训练集拟合效果，\n",
    "# 较小的ν意味着我们需要更多的弱学习器的迭代次数。\n",
    "# 通常用步长和迭代最大次数一起来决定算法的拟合效果。\n",
    "# 所以这两个参数n_estimators和learning_rate要一起调参。\n",
    "# 一般来说，可以从一个小一点的ν开始调参，默认是1。\n",
    "learning_rate = 1.0\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "clf = ensemble.AdaBoostClassifier(\n",
    "    n_estimators=n_estimators, learning_rate=learning_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "开始训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train set = 57.91%\n",
      "Accuracy on test set = 55.62%\n",
      "Log-loss on test set = 69.24820\n"
     ]
    }
   ],
   "source": [
    "x_train_trans_arr = scaler.fit_transform(x_train_arr)\n",
    "# print 'type(X_train_trans),X_train_trans[:5,:]\\n%s\\n%s'%(type(X_train_trans),X_train_trans[:5,:])\n",
    "# print 'type(Y_train),Y_train.head()\\n%s\\n%s'%(type(Y_train),Y_train.head())\n",
    "clf.fit(x_train_trans_arr, y_train_arr)\n",
    "# 交叉检验\n",
    "y_pred = clf.predict(x_train_trans_arr)\n",
    "print('Accuracy on train set = {:.2f}%'.format(metrics.accuracy_score(y_train_arr, y_pred) * 100))\n",
    "x_test_trans = scaler.transform(x_test_arr)\n",
    "y_pred = clf.predict(x_test_trans)\n",
    "y_pred_prob = clf.predict_proba(x_test_trans)\n",
    "print('Accuracy on test set = {:.2f}%'.format(metrics.accuracy_score(y_test_arr, y_pred) * 100))\n",
    "print('Log-loss on test set = {:.5f}'.format(metrics.log_loss(y_test_arr, y_pred_prob) * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "目标结果占比情况\n",
      "-1 共 4031 次，占比54.03%\n",
      " 1 共 3430 次，占比45.97%\n",
      "预测结果占比情况\n",
      " 1 共 2717 次，占比36.42%\n",
      "-1 共 4744 次，占比63.58%\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>次数(目标)</th>\n",
       "      <th>占比(目标)</th>\n",
       "      <th>次数(预测)</th>\n",
       "      <th>占比(预测)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-1.0000</th>\n",
       "      <td>4,031.0000</td>\n",
       "      <td>54.0276</td>\n",
       "      <td>4,744.0000</td>\n",
       "      <td>63.5840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0000</th>\n",
       "      <td>3,430.0000</td>\n",
       "      <td>45.9724</td>\n",
       "      <td>2,717.0000</td>\n",
       "      <td>36.4160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            次数(目标)  占比(目标)     次数(预测)  占比(预测)\n",
       "value                                        \n",
       "-1.0000 4,031.0000 54.0276 4,744.0000 63.5840\n",
       "1.0000  3,430.0000 45.9724 2,717.0000 36.4160"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('目标结果占比情况')\n",
    "for value, count in Counter(y_test_arr).items():\n",
    "    print(f'{int(value):2d} 共 {count:4d} 次，占比{count/y_test_arr.shape[0]*100:5.2f}%')\n",
    "print('预测结果占比情况')\n",
    "for value, count in Counter(y_pred).items():\n",
    "    print(f'{int(value):2d} 共 {count:4d} 次，占比{count/y_pred.shape[0]*100:5.2f}%')\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {value: {'value':value, '次数': count, '占比': count / y_test_arr.shape[0] * 100}\n",
    "             for value, count in Counter(y_test_arr).items()},\n",
    ").T.set_index('value').join(pd.DataFrame(\n",
    "    {value: {'value':value, '次数': count, '占比': count / y_pred.shape[0] * 100}\n",
    "             for value, count in Counter(y_pred).items()},\n",
    ").T.set_index('value'), how='outer', on='value', lsuffix='(目标)', rsuffix='(预测)')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "开始交叉验证训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n= 20 rate=1.00 mean_res=49.39, std_res=2.67711, result=[45.98436389 52.74569042 49.72454238 46.72116581 51.78603163]\n",
      "n= 30 rate=1.00 mean_res=48.65, std_res=3.13261, result=[42.83937456 50.89745868 49.26248445 48.44499733 51.78603163]\n",
      "n= 40 rate=1.00 mean_res=49.40, std_res=2.15718, result=[46.35749822 51.41283099 50.02665719 47.43202417 51.78603163]\n",
      "n= 50 rate=1.00 mean_res=50.52, std_res=1.29570, result=[52.30987918 50.07997157 49.26248445 49.17362715 51.78603163]\n",
      "n= 60 rate=1.00 mean_res=48.03, std_res=3.92621, result=[41.29353234 52.14146081 47.62751022 47.30762396 51.78603163]\n",
      "n= 70 rate=1.00 mean_res=47.57, std_res=3.86456, result=[40.45842217 49.65345655 48.88928381 47.07659499 51.78603163]\n",
      "n= 20 rate=0.50 mean_res=47.55, std_res=4.60973, result=[40.90262971 51.43060245 50.55980096 43.07801671 51.78603163]\n",
      "n= 30 rate=0.50 mean_res=48.06, std_res=4.34853, result=[41.43567875 51.55500267 51.25288786 44.26870446 51.78603163]\n",
      "n= 40 rate=0.50 mean_res=48.18, std_res=4.41381, result=[41.45344705 52.49688999 50.71974409 44.42864759 51.78603163]\n",
      "n= 50 rate=0.50 mean_res=48.34, std_res=3.47818, result=[43.49680171 52.03483206 49.29802737 45.08619158 51.78603163]\n",
      "n= 60 rate=0.50 mean_res=47.70, std_res=4.46091, result=[40.45842217 52.14146081 49.26248445 44.83739115 51.78603163]\n",
      "n= 70 rate=0.50 mean_res=47.74, std_res=4.40591, result=[40.49395878 52.28363249 48.88928381 45.24613471 51.78603163]\n",
      "n= 20 rate=0.10 mean_res=48.55, std_res=6.03310, result=[39.33901919 51.55500267 56.08672472 43.98436112 51.78603163]\n",
      "n= 30 rate=0.10 mean_res=48.51, std_res=5.73408, result=[39.76545842 51.64385996 55.3580949  43.98436112 51.78603163]\n",
      "n= 40 rate=0.10 mean_res=48.28, std_res=5.47375, result=[39.62331201 51.64385996 54.14963569 44.17984717 51.78603163]\n",
      "n= 50 rate=0.10 mean_res=48.29, std_res=5.39964, result=[39.78322672 51.82157455 53.91860672 44.16207571 51.78603163]\n",
      "n= 60 rate=0.10 mean_res=48.19, std_res=5.28639, result=[39.85429993 51.82157455 53.40323441 44.07321841 51.78603163]\n",
      "n= 70 rate=0.10 mean_res=48.08, std_res=5.18074, result=[39.92537313 51.78603163 52.92340501 43.98436112 51.78603163]\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "x_trans_arr = scaler.fit_transform(x_arr)\n",
    "for rate, n in itertools.product([1.0, 0.5, 0.1], range(20, 80, 10)):\n",
    "    estimator = ensemble.AdaBoostClassifier(\n",
    "    n_estimators=n, learning_rate=rate)\n",
    "    result = np.array(cross_val_score(estimator, x_trans_arr, y_arr)) * 100\n",
    "    print(f'n={n:3d} rate={rate:.2f} mean_res={np.mean(result):.2f}, std_res={np.std(result):.5f}, result={result}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n= 20 rate=1.00 mean_res=51.75, std_res=2.69114, result=[46.60233213 52.61359067 51.80940893 54.11220591 53.58938267]\n",
      "n= 30 rate=1.00 mean_res=51.46, std_res=2.69362, result=[46.50180941 51.58825895 51.50784077 54.1323145  53.58938267]\n",
      "n= 40 rate=1.00 mean_res=51.57, std_res=2.80501, result=[46.38118215 51.32689988 52.15118617 54.37361753 53.60949125]\n",
      "n= 50 rate=1.00 mean_res=51.44, std_res=2.48015, result=[46.70285485 51.72899075 51.86972256 53.26764528 53.60949125]\n",
      "n= 60 rate=1.00 mean_res=51.23, std_res=2.42445, result=[46.70285485 51.4274226  51.4274226  53.00623366 53.60949125]\n",
      "n= 70 rate=1.00 mean_res=51.09, std_res=2.37126, result=[46.60233213 51.66867712 51.40731805 52.18178162 53.60949125]\n",
      "n= 20 rate=0.50 mean_res=52.24, std_res=2.85363, result=[46.6827503  52.59348613 53.6389224  54.67524633 53.58938267]\n",
      "n= 30 rate=0.50 mean_res=52.01, std_res=2.85115, result=[46.62243667 51.72899075 53.47808605 54.63502916 53.58938267]\n",
      "n= 40 rate=0.50 mean_res=51.79, std_res=2.77764, result=[46.5420185  51.50784077 53.03578609 54.2730746  53.58938267]\n",
      "n= 50 rate=0.50 mean_res=51.91, std_res=2.75322, result=[46.58222758 52.01045436 53.29714515 54.09209733 53.58938267]\n",
      "n= 60 rate=0.50 mean_res=51.84, std_res=2.68580, result=[46.62243667 52.29191797 52.61359067 54.07198874 53.58938267]\n",
      "n= 70 rate=0.50 mean_res=51.91, std_res=2.76666, result=[46.60233213 52.2114998  52.69400885 54.47416047 53.58938267]\n",
      "n= 20 rate=0.10 mean_res=52.71, std_res=3.07187, result=[46.62243667 54.04101327 54.38279051 54.89644078 53.58938267]\n",
      "n= 30 rate=0.10 mean_res=52.71, std_res=3.00761, result=[46.76316848 53.7796542  54.58383595 54.81600643 53.58938267]\n",
      "n= 40 rate=0.10 mean_res=52.57, std_res=2.91282, result=[46.80337756 53.6389224  54.14153599 54.69535492 53.58938267]\n",
      "n= 50 rate=0.10 mean_res=52.47, std_res=2.86960, result=[46.82348211 53.05589063 54.32247688 54.53448623 53.58938267]\n",
      "n= 60 rate=0.10 mean_res=52.36, std_res=2.82341, result=[46.84358665 52.75432248 54.1013269  54.53448623 53.58938267]\n",
      "n= 70 rate=0.10 mean_res=52.36, std_res=2.79820, result=[46.88379574 52.81463611 53.98069964 54.55459481 53.58938267]\n"
     ]
    }
   ],
   "source": [
    "import itertools\n",
    "\n",
    "scaler = preprocessing.MinMaxScaler()\n",
    "x_trans_arr = scaler.fit_transform(x_arr)\n",
    "for rate, n in itertools.product([1.0, 0.5, 0.1], range(20, 80, 10)):\n",
    "    estimator = ensemble.AdaBoostClassifier(\n",
    "    n_estimators=n, learning_rate=rate)\n",
    "    result = np.array(cross_val_score(estimator, x_trans_arr, y_arr)) * 100\n",
    "    print(f'n={n:3d} rate={rate:.2f} mean_res={np.mean(result):.2f}, std_res={np.std(result):.5f}, result={result}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importances = pd.Series(clf.feature_importances_, index=factor_df.columns) * 100\n",
    "feature_importances.sort_values(ascending=False, inplace=True)\n",
    "ax = feature_importances.plot(kind='bar', figsize=(20,10))\n",
    "ax.set(ylabel='Importance', title='Feature importances');\n",
    "ax.set_yscale(\"log\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尝试使用近20天的数据，滚动向前，查看预测未来5天的Y值准确率，以及 feature importance 的变化情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_to 2020-02-06 00:00:00 训练完成\n",
      "date_to 2020-02-07 00:00:00 训练完成\n",
      "date_to 2020-02-10 00:00:00 训练完成\n",
      "date_to 2020-02-11 00:00:00 训练完成\n",
      "date_to 2020-02-12 00:00:00 训练完成\n",
      "date_to 2020-02-13 00:00:00 训练完成\n",
      "date_to 2020-02-14 00:00:00 训练完成\n",
      "date_to 2020-02-17 00:00:00 训练完成\n",
      "date_to 2020-02-18 00:00:00 训练完成\n",
      "date_to 2020-02-19 00:00:00 训练完成\n",
      "date_to 2020-02-20 00:00:00 训练完成\n",
      "date_to 2020-02-21 00:00:00 训练完成\n",
      "date_to 2020-02-24 00:00:00 训练完成\n",
      "date_to 2020-02-25 00:00:00 训练完成\n",
      "date_to 2020-02-26 00:00:00 训练完成\n",
      "date_to 2020-02-27 00:00:00 训练完成\n",
      "date_to 2020-02-28 00:00:00 训练完成\n",
      "date_to 2020-03-02 00:00:00 训练完成\n",
      "date_to 2020-03-03 00:00:00 训练完成\n",
      "date_to 2020-03-04 00:00:00 训练完成\n",
      "date_to 2020-03-05 00:00:00 训练完成\n",
      "date_to 2020-03-06 00:00:00 训练完成\n",
      "date_to 2020-03-09 00:00:00 训练完成\n",
      "date_to 2020-03-10 00:00:00 训练完成\n",
      "date_to 2020-03-11 00:00:00 未来n日预测结果只有一个结果 -1.0\n",
      "date_to 2020-03-12 00:00:00 训练完成\n",
      "date_to 2020-03-13 00:00:00 训练完成\n",
      "date_to 2020-03-16 00:00:00 训练完成\n",
      "date_to 2020-03-17 00:00:00 训练完成\n",
      "date_to 2020-03-18 00:00:00 训练完成\n",
      "date_to 2020-03-19 00:00:00 训练完成\n",
      "date_to 2020-03-20 00:00:00 未来n日预测结果只有一个结果 1.0\n",
      "date_to 2020-03-23 00:00:00 训练完成\n",
      "date_to 2020-03-24 00:00:00 训练完成\n",
      "date_to 2020-03-25 00:00:00 训练完成\n",
      "date_to 2020-03-26 00:00:00 训练完成\n",
      "date_to 2020-03-27 00:00:00 训练完成\n",
      "date_to 2020-03-30 00:00:00 训练完成\n",
      "date_to 2020-03-31 00:00:00 训练完成\n",
      "date_to 2020-04-01 00:00:00 训练完成\n",
      "date_to 2020-04-02 00:00:00 训练完成\n",
      "date_to 2020-04-03 00:00:00 未来n日预测结果只有一个结果 -1.0\n",
      "date_to 2020-04-07 00:00:00 训练完成\n",
      "date_to 2020-04-08 00:00:00 训练完成\n",
      "date_to 2020-04-09 00:00:00 训练完成\n",
      "date_to 2020-04-10 00:00:00 训练完成\n",
      "date_to 2020-04-13 00:00:00 训练完成\n",
      "date_to 2020-04-14 00:00:00 训练完成\n",
      "date_to 2020-04-15 00:00:00 训练完成\n",
      "date_to 2020-04-16 00:00:00 训练完成\n",
      "date_to 2020-04-17 00:00:00 训练完成\n",
      "date_to 2020-04-20 00:00:00 训练完成\n",
      "date_to 2020-04-21 00:00:00 训练完成\n",
      "date_to 2020-04-22 00:00:00 训练完成\n",
      "date_to 2020-04-23 00:00:00 训练完成\n",
      "date_to 2020-04-24 00:00:00 训练完成\n",
      "date_to 2020-04-27 00:00:00 训练完成\n",
      "date_to 2020-04-28 00:00:00 训练完成\n",
      "date_to 2020-04-29 00:00:00 训练完成\n",
      "date_to 2020-04-30 00:00:00 训练完成\n",
      "date_to 2020-05-06 00:00:00 训练完成\n",
      "date_to 2020-05-07 00:00:00 训练完成\n",
      "date_to 2020-05-08 00:00:00 训练完成\n",
      "date_to 2020-05-11 00:00:00 训练完成\n",
      "date_to 2020-05-12 00:00:00 训练完成\n",
      "date_to 2020-05-13 00:00:00 训练完成\n",
      "date_to 2020-05-14 00:00:00 训练完成\n",
      "date_to 2020-05-15 00:00:00 训练完成\n",
      "date_to 2020-05-18 00:00:00 训练完成\n",
      "date_to 2020-05-19 00:00:00 训练完成\n",
      "date_to 2020-05-20 00:00:00 训练完成\n",
      "date_to 2020-05-21 00:00:00 训练完成\n",
      "date_to 2020-05-22 00:00:00 训练完成\n",
      "date_to 2020-05-25 00:00:00 训练完成\n",
      "date_to 2020-05-26 00:00:00 训练完成\n",
      "date_to 2020-05-27 00:00:00 训练完成\n",
      "date_to 2020-05-28 00:00:00 训练完成\n",
      "date_to 2020-05-29 00:00:00 训练完成\n",
      "date_to 2020-06-01 00:00:00 训练完成\n",
      "date_to 2020-06-02 00:00:00 训练完成\n",
      "date_to 2020-06-03 00:00:00 训练完成\n",
      "date_to 2020-06-04 00:00:00 训练完成\n",
      "date_to 2020-06-05 00:00:00 训练完成\n"
     ]
    }
   ],
   "source": [
    "win_size_days = 20\n",
    "predict_future_n_days = 5\n",
    "\n",
    "def get_available_arr(dates, date_s, x_df, y_arr):\n",
    "    date_from, date_to = pd.to_datetime(dates.min()), pd.to_datetime(dates.max())\n",
    "    sub_available = ((date_from<=date_s) & (date_s<date_to)).to_numpy()\n",
    "    sub_factor_df = x_df[sub_available]\n",
    "    sub_y_arr = y_arr[sub_available]\n",
    "    return date_from, date_to, sub_factor_df, sub_y_arr\n",
    "\n",
    "def train_and_valid(available_factor_df, y_arr, win_size_days, predict_future_n_days):\n",
    "    # 有效历史数据的日期序列\n",
    "    available_factor_df_date_s = pd.Series(available_factor_df.index, index=available_factor_df.index).apply(lambda x:x.date())\n",
    "    # Unique 日期序列\n",
    "    date_s = pd.Series(available_factor_df_date_s.unique())\n",
    "    predict_dic = {}\n",
    "    for idx in range(win_size_days, date_s.shape[0]-predict_future_n_days):\n",
    "        # 建立训练集\n",
    "        date_from, date_to, sub_factor_df, sub_y_train_arr =get_available_arr(\n",
    "            date_s.iloc[idx-win_size_days:idx], available_factor_df_date_s, available_factor_df, y_arr)\n",
    "        # 建立分类器\n",
    "        estimator = ensemble.AdaBoostClassifier(n_estimators=n, learning_rate=rate)\n",
    "        scaler = preprocessing.MinMaxScaler()\n",
    "        sub_x_trans_arr = scaler.fit_transform(sub_factor_df.to_numpy())\n",
    "        # 训练\n",
    "        clf.fit(sub_x_trans_arr, sub_y_train_arr)\n",
    "        # 建立验证集\n",
    "        _, _, sub_factor_df, sub_y_test_arr =get_available_arr(\n",
    "            date_s.iloc[idx:idx+predict_future_n_days], available_factor_df_date_s, available_factor_df, y_arr)\n",
    "        # 交叉检验\n",
    "        sub_y_train_pred = clf.predict(sub_x_trans_arr)\n",
    "        # print('Accuracy on train set = {:.2f}%'.format(metrics.accuracy_score(sub_y_train_arr, sub_y_pred) * 100))\n",
    "        sub_y_test_pred = clf.predict(scaler.transform(sub_factor_df.to_numpy()))\n",
    "        sub_y_pred_prob = clf.predict_proba(scaler.transform(sub_factor_df.to_numpy()))\n",
    "        # print('Accuracy on test set = {:.2f}%'.format(metrics.accuracy_score(sub_y_test_arr, sub_y_pred) * 100))\n",
    "        # print('Log-loss on test set = {:.5f}%'.format(metrics.log_loss(sub_y_test_arr, sub_y_pred_prob) * 100))\n",
    "        predict_dic[date_to] = {\n",
    "            'Accuracy on train': metrics.accuracy_score(sub_y_train_arr, sub_y_train_pred) * 100,\n",
    "            'Accuracy on test': metrics.accuracy_score(sub_y_test_arr, sub_y_test_pred) * 100,\n",
    "            'Log-loss on test': metrics.log_loss(sub_y_test_arr, sub_y_pred_prob) * 100,\n",
    "        }\n",
    "        if len(np.unique(sub_y_test_pred)) == 1:\n",
    "            print(f'date_to {date_to} 未来n日预测结果只有一个结果 {np.unique(sub_y_test_pred)[0]}')\n",
    "        else:\n",
    "            print(f'date_to {date_to} 训练完成')\n",
    "\n",
    "predict_df = train_and_valid(available_factor_df, y_arr, win_size_days, predict_future_n_days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>target</th>\n",
       "      <th>predict</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-06-08 09:02:00+08:00</th>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-08 09:03:00+08:00</th>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-08 09:04:00+08:00</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-08 09:05:00+08:00</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-08 09:06:00+08:00</th>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12 08:54:00+08:00</th>\n",
       "      <td>1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12 08:55:00+08:00</th>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12 08:56:00+08:00</th>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12 08:57:00+08:00</th>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-12 08:58:00+08:00</th>\n",
       "      <td>-1.0000</td>\n",
       "      <td>-1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>904 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           target  predict\n",
       "datetime                                  \n",
       "2020-06-08 09:02:00+08:00 -1.0000  -1.0000\n",
       "2020-06-08 09:03:00+08:00 -1.0000  -1.0000\n",
       "2020-06-08 09:04:00+08:00  1.0000  -1.0000\n",
       "2020-06-08 09:05:00+08:00  1.0000  -1.0000\n",
       "2020-06-08 09:06:00+08:00 -1.0000  -1.0000\n",
       "...                           ...      ...\n",
       "2020-06-12 08:54:00+08:00  1.0000  -1.0000\n",
       "2020-06-12 08:55:00+08:00 -1.0000  -1.0000\n",
       "2020-06-12 08:56:00+08:00 -1.0000  -1.0000\n",
       "2020-06-12 08:57:00+08:00 -1.0000  -1.0000\n",
       "2020-06-12 08:58:00+08:00 -1.0000  -1.0000\n",
       "\n",
       "[904 rows x 2 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({'target':sub_y_test_arr, 'predict':sub_y_test_pred})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy on train</th>\n",
       "      <th>Accuracy on test</th>\n",
       "      <th>Log-loss on test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>83.2674</td>\n",
       "      <td>68.5575</td>\n",
       "      <td>62.7726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>81.8760</td>\n",
       "      <td>68.7023</td>\n",
       "      <td>64.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>81.7248</td>\n",
       "      <td>69.6921</td>\n",
       "      <td>64.4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>80.0597</td>\n",
       "      <td>70.3892</td>\n",
       "      <td>63.7530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>78.4332</td>\n",
       "      <td>70.1299</td>\n",
       "      <td>63.5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>67.3746</td>\n",
       "      <td>68.9792</td>\n",
       "      <td>65.9516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>68.1996</td>\n",
       "      <td>70.5419</td>\n",
       "      <td>64.3474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>67.7393</td>\n",
       "      <td>69.3215</td>\n",
       "      <td>66.5333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>68.0083</td>\n",
       "      <td>71.5655</td>\n",
       "      <td>64.5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>68.1246</td>\n",
       "      <td>72.0133</td>\n",
       "      <td>60.4078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Accuracy on train  Accuracy on test  Log-loss on test\n",
       "2020-02-06            83.2674           68.5575           62.7726\n",
       "2020-02-07            81.8760           68.7023           64.8792\n",
       "2020-02-10            81.7248           69.6921           64.4444\n",
       "2020-02-11            80.0597           70.3892           63.7530\n",
       "2020-02-12            78.4332           70.1299           63.5334\n",
       "...                       ...               ...               ...\n",
       "2020-06-01            67.3746           68.9792           65.9516\n",
       "2020-06-02            68.1996           70.5419           64.3474\n",
       "2020-06-03            67.7393           69.3215           66.5333\n",
       "2020-06-04            68.0083           71.5655           64.5037\n",
       "2020-06-05            68.1246           72.0133           60.4078\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "predict_df = pd.DataFrame(predict_dic).T\n",
    "predict_df.plot()\n",
    "predict_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date_to 2020-02-20 00:00:00 训练完成\n",
      "date_to 2020-02-21 00:00:00 训练完成\n",
      "date_to 2020-02-24 00:00:00 训练完成\n",
      "date_to 2020-02-25 00:00:00 训练完成\n",
      "date_to 2020-02-26 00:00:00 训练完成\n",
      "date_to 2020-02-27 00:00:00 训练完成\n",
      "date_to 2020-02-28 00:00:00 训练完成\n",
      "date_to 2020-03-02 00:00:00 训练完成\n",
      "date_to 2020-03-03 00:00:00 训练完成\n",
      "date_to 2020-03-04 00:00:00 训练完成\n",
      "date_to 2020-03-05 00:00:00 训练完成\n",
      "date_to 2020-03-06 00:00:00 训练完成\n",
      "date_to 2020-03-09 00:00:00 训练完成\n",
      "date_to 2020-03-10 00:00:00 训练完成\n",
      "date_to 2020-03-11 00:00:00 训练完成\n",
      "date_to 2020-03-12 00:00:00 训练完成\n",
      "date_to 2020-03-13 00:00:00 训练完成\n",
      "date_to 2020-03-16 00:00:00 训练完成\n",
      "date_to 2020-03-17 00:00:00 训练完成\n",
      "date_to 2020-03-18 00:00:00 训练完成\n",
      "date_to 2020-03-19 00:00:00 训练完成\n",
      "date_to 2020-03-20 00:00:00 训练完成\n",
      "date_to 2020-03-23 00:00:00 未来n日预测结果只有一个结果 1.0\n",
      "date_to 2020-03-24 00:00:00 训练完成\n",
      "date_to 2020-03-25 00:00:00 训练完成\n",
      "date_to 2020-03-26 00:00:00 训练完成\n",
      "date_to 2020-03-27 00:00:00 训练完成\n",
      "date_to 2020-03-30 00:00:00 未来n日预测结果只有一个结果 -1.0\n",
      "date_to 2020-03-31 00:00:00 训练完成\n",
      "date_to 2020-04-01 00:00:00 训练完成\n",
      "date_to 2020-04-02 00:00:00 训练完成\n",
      "date_to 2020-04-03 00:00:00 未来n日预测结果只有一个结果 -1.0\n",
      "date_to 2020-04-07 00:00:00 训练完成\n",
      "date_to 2020-04-08 00:00:00 训练完成\n",
      "date_to 2020-04-09 00:00:00 训练完成\n",
      "date_to 2020-04-10 00:00:00 训练完成\n",
      "date_to 2020-04-13 00:00:00 训练完成\n",
      "date_to 2020-04-14 00:00:00 训练完成\n",
      "date_to 2020-04-15 00:00:00 训练完成\n",
      "date_to 2020-04-16 00:00:00 训练完成\n",
      "date_to 2020-04-17 00:00:00 训练完成\n",
      "date_to 2020-04-20 00:00:00 训练完成\n",
      "date_to 2020-04-21 00:00:00 训练完成\n",
      "date_to 2020-04-22 00:00:00 训练完成\n",
      "date_to 2020-04-23 00:00:00 训练完成\n",
      "date_to 2020-04-24 00:00:00 训练完成\n",
      "date_to 2020-04-27 00:00:00 训练完成\n",
      "date_to 2020-04-28 00:00:00 训练完成\n",
      "date_to 2020-04-29 00:00:00 训练完成\n",
      "date_to 2020-04-30 00:00:00 训练完成\n",
      "date_to 2020-05-06 00:00:00 训练完成\n",
      "date_to 2020-05-07 00:00:00 训练完成\n",
      "date_to 2020-05-08 00:00:00 训练完成\n",
      "date_to 2020-05-11 00:00:00 训练完成\n",
      "date_to 2020-05-12 00:00:00 训练完成\n",
      "date_to 2020-05-13 00:00:00 训练完成\n",
      "date_to 2020-05-14 00:00:00 训练完成\n",
      "date_to 2020-05-15 00:00:00 训练完成\n",
      "date_to 2020-05-18 00:00:00 未来n日预测结果只有一个结果 -1.0\n",
      "date_to 2020-05-19 00:00:00 训练完成\n",
      "date_to 2020-05-20 00:00:00 训练完成\n",
      "date_to 2020-05-21 00:00:00 训练完成\n",
      "date_to 2020-05-22 00:00:00 训练完成\n",
      "date_to 2020-05-25 00:00:00 训练完成\n",
      "date_to 2020-05-26 00:00:00 训练完成\n",
      "date_to 2020-05-27 00:00:00 训练完成\n",
      "date_to 2020-05-28 00:00:00 训练完成\n",
      "date_to 2020-05-29 00:00:00 训练完成\n",
      "date_to 2020-06-01 00:00:00 训练完成\n",
      "date_to 2020-06-02 00:00:00 训练完成\n",
      "date_to 2020-06-03 00:00:00 训练完成\n",
      "date_to 2020-06-04 00:00:00 未来n日预测结果只有一个结果 1.0\n",
      "date_to 2020-06-05 00:00:00 训练完成\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy on train</th>\n",
       "      <th>Accuracy on test</th>\n",
       "      <th>Log-loss on test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>83.2674</td>\n",
       "      <td>68.5575</td>\n",
       "      <td>62.7726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>81.8760</td>\n",
       "      <td>68.7023</td>\n",
       "      <td>64.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>81.7248</td>\n",
       "      <td>69.6921</td>\n",
       "      <td>64.4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>80.0597</td>\n",
       "      <td>70.3892</td>\n",
       "      <td>63.7530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>78.4332</td>\n",
       "      <td>70.1299</td>\n",
       "      <td>63.5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>67.3746</td>\n",
       "      <td>68.9792</td>\n",
       "      <td>65.9516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>68.1996</td>\n",
       "      <td>70.5419</td>\n",
       "      <td>64.3474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>67.7393</td>\n",
       "      <td>69.3215</td>\n",
       "      <td>66.5333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>68.0083</td>\n",
       "      <td>71.5655</td>\n",
       "      <td>64.5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>68.1246</td>\n",
       "      <td>72.0133</td>\n",
       "      <td>60.4078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Accuracy on train  Accuracy on test  Log-loss on test\n",
       "2020-02-06            83.2674           68.5575           62.7726\n",
       "2020-02-07            81.8760           68.7023           64.8792\n",
       "2020-02-10            81.7248           69.6921           64.4444\n",
       "2020-02-11            80.0597           70.3892           63.7530\n",
       "2020-02-12            78.4332           70.1299           63.5334\n",
       "...                       ...               ...               ...\n",
       "2020-06-01            67.3746           68.9792           65.9516\n",
       "2020-06-02            68.1996           70.5419           64.3474\n",
       "2020-06-03            67.7393           69.3215           66.5333\n",
       "2020-06-04            68.0083           71.5655           64.5037\n",
       "2020-06-05            68.1246           72.0133           60.4078\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "win_size_days = 30\n",
    "predict_future_n_days = 5\n",
    "predict_df = train_and_valid(available_factor_df, y_arr, win_size_days, predict_future_n_days)\n",
    "predict_df = pd.DataFrame(predict_dic).T\n",
    "predict_df.plot()\n",
    "predict_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy on train</th>\n",
       "      <th>Accuracy on test</th>\n",
       "      <th>Log-loss on test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>83.2674</td>\n",
       "      <td>68.5575</td>\n",
       "      <td>62.7726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>81.8760</td>\n",
       "      <td>68.7023</td>\n",
       "      <td>64.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>81.7248</td>\n",
       "      <td>69.6921</td>\n",
       "      <td>64.4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>80.0597</td>\n",
       "      <td>70.3892</td>\n",
       "      <td>63.7530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>78.4332</td>\n",
       "      <td>70.1299</td>\n",
       "      <td>63.5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>67.3746</td>\n",
       "      <td>68.9792</td>\n",
       "      <td>65.9516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>68.1996</td>\n",
       "      <td>70.5419</td>\n",
       "      <td>64.3474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>67.7393</td>\n",
       "      <td>69.3215</td>\n",
       "      <td>66.5333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>68.0083</td>\n",
       "      <td>71.5655</td>\n",
       "      <td>64.5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>68.1246</td>\n",
       "      <td>72.0133</td>\n",
       "      <td>60.4078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Accuracy on train  Accuracy on test  Log-loss on test\n",
       "2020-02-06            83.2674           68.5575           62.7726\n",
       "2020-02-07            81.8760           68.7023           64.8792\n",
       "2020-02-10            81.7248           69.6921           64.4444\n",
       "2020-02-11            80.0597           70.3892           63.7530\n",
       "2020-02-12            78.4332           70.1299           63.5334\n",
       "...                       ...               ...               ...\n",
       "2020-06-01            67.3746           68.9792           65.9516\n",
       "2020-06-02            68.1996           70.5419           64.3474\n",
       "2020-06-03            67.7393           69.3215           66.5333\n",
       "2020-06-04            68.0083           71.5655           64.5037\n",
       "2020-06-05            68.1246           72.0133           60.4078\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBRgYGDB79mxGjBjByJEjAejduzfTp0/nqaeeYsyYMeXme5BLxleENG01EkM629CttTmf7w0hI7fqEtESiaTuiCrqB86YMQMvLy+GDx9eo3N0dHQ4ffo0Y8eOZfv27QwdOhSAlStX8sEHH3D9+nW8vLxITEyssQx1LRl/7tw5zp07x8WLF9mzZw+gloyfP38+AQEBdO3atdq5Ggq5ImkkFEXhrREdGf3Ncb4/HMHLg9trWiSJpE7UduXQUJiZmWFubs6RI0d45JFHWLduHf369cPc3BwTExNOnjxJjx49yrTlXb16dYVzubq6EhkZSVhYGC4uLqVzZWRkkJWVxfDhw+nRowcuLi4AhIeH0717d7p3784ff/zB9evXsbS0LJ3vQS4ZXxFSkTQiXRzNGdrZljXHrvJc/7YY6GprWiSJpMmQlZVVahICePnll/n555+ZN28eWVlZtGnTplRR/PTTT8yZM4dmzZrRv3//agspGhgYsHr1asaPH09BQQE+Pj7MmzePpKQknnzyydKmVsuWLQNg0aJFhIaGIoRg0KBBeHp6lplv9OjRnDhxAk9Pz9IGXLa2tjVWJCUl4w0NDTlx4gRbtmzhhRdeIDU1lYKCAhYuXEj79u2ZMmUKqampCCHKlIwfN24cv//+O1999RWPPPJIbX7NdaJJlJGvTxqzjHxFHA6JZ+qq06x42psRHnYak0MiqQ1NrYx8RkZGad7JRx99xM2bN/niiy80LFXT4UEtI//A0NvFCltTA347E61pUSSSB5Zdu3bh5eWFm5sbR44c4e2339a0SA800rTVyGhrKYzq0oofjkQQn55LCxP96k+SSCS1YsKECUyYMEHTYjw0yBWJBhjXtRWFRYLfz8VoWhSJRCK5Z6Qi0QAu1iZ42puxJUCatyRNh4fNn/qwUpe/s1QkGmJsV3uCYtM5EZ5Y/WCJRMMYGBiQmJgolckDjhCCxMREDAwManWe9JFoiCc9W7HyUDhTV51i0ZAOzO7TBi0tRdNiSSQVYm9vT3R0NPHx8ZoWRdLAGBgYlAmzrgky/FeDJGfm8cbWi+y+FEsfFys+e8oTG9PaPQlIJBJJYyDDf+9TzJvp8e0Ubz4c407AtWSGLj/MnkuxmhZLIpFIaoVUJBpGURQm+Tqy84U+tDI3ZO66AN7cdpHsvEJNiyaRSCQ1QiqS+4S2LYzZ+mxv/tW3DRtORTHyqyNcupGqabEkEomkWqQiuY/Q09HijeEdWT+7Oxm5BUz8/qRcmUgkkvseqUjuQ3q7WLFsghfpOQUcDL6laXEkEomkSqQiuU/p7myJlbEeuy7c1LQoEolEUiVNQpEoitJBUZRzd7zSFEVZqCjKEkVRYu7YP7z62ZoG2loKw9zsOBB0i6w82QhLIpHcvzQJRSKECBZCeAkhvICuQBawrfjwspJjQog/NSdl/TPc3Y7s/EIOBskkMIlEcv/SJBTJXQwCwoUQ1zQtSEPj62yBlbE+uy7eqHxQUgT8sRDysxtPMIlEIrmDpqhIJgIb73i/QFGUC4qirFIUxVxTQjUE2loKw91tqzZvXf4dAlbDxS2NK5xEIpEU06QUiaIoesATwObiXd8CbQEv4CbwWSXnzVUUxV9RFP+mVitouLsdOflF7L0cV/GA+BB1e/p7eMjK3UgkkvuDJqVIgGHAGSFEHIAQIk4IUSiEKAJ+AHwrOkkI8b0QopsQoluLFi0aUdx7x8fJgrYtmrF4xyXC4zPKD4gPAi0diL0A0X6NL6BEInnoaWqKZBJ3mLUURbmz6floILDRJWpgtLUUVk33QUdLYdqq09xKz7l9UAhICAWPCaBvpq5KJBKJpJFpMopEURQjYDCw9Y7dSxVFuagoygVgAPCSRoRrYFpbNmPVdB+SMvOYucaP/MIi9UDaDchLh5ZdoMtkuLQd0isxgUkkEkkD0WQUiRAiSwhhKYRIvWPfM0IIdyGEhxDiCSHEA5u952HfnI/HehAYk8afF4s/ZkKwum3RAXxmQ1E+nPlZc0JKJJKHkiajSCQwwt0OF2tjVv4ToXaqiy9WJFYdwLItuDwK/qugMF+zgkokkocKqUiaEFpaCnP7tuHKzTQOhyaoisSgORhbqwN85kD6TQjaqVlBJRLJQ4VUJE2MJ71aYmOqz3f/hENCiGrWUopb9LYbDM1bw+kfNCukRCJ5qJCKpImhr6PNrD7OHA9PpCAuCKza3z6opa36Sq4dg7hLmhNSIpE8VEhF0gSZ5OuIvX4WOjmJ6orkTrpMAR0DuSqRSCSNhlQkTRATA13GOWYBUGh5lyIxsgD38XDhF8hO0YB0EonkYUMqkiZKf8skAC7n25Y/6DsH8rPg3IZGlkoikTyMSEXSROmofZMsoc+f13XKH7TzBIfu4PcDFBU1vnASieShQiqSJop+Shhxeg7sr6xXie9ctcR8+IHGFUwikTx0SEXSVEkIodCiHSFxGUQlZpU/3vEJaGatrkokEomkAZGKpCkiBKTH0qJVGwD2XamgvpaOHnSbASF/Q9LVRhZQIpE8TEhF0hTJTYeifMwsbXGxNmZ/UCWFGrvOUHNL/H9qXPkkEslDhVQkTZGsBHXbzIpHO9pwKiKJUxGJ5ceZ2kHrXnDteOPKJ5FIHiqkImmKZKmhvxhZMrm7I63MDZn4w0k++Tvodon5Epo7QmpM48sokUjuLxqwg6pUJE2RrOLVh5EVDhZG7HrhEcZ3tWfFwXDGfnuciDs7KZo5QEYcFORpRlaJRKJ5ctNh1RAI3t0g00tF0hTJLDZtGVkAYKyvw9Jxnqyc4k1UUhYjvjzKxtNRaql5M3tAQPoNzckrkUg0R1Eh/DZHbcWtXUHeWT0gFUlTpHRFYllm91A3O3a/2Jeurc15Y+tF5q4LIE3PRj2YGt3IQkokkvuCfUsg5C8YtlTtWdQASEXSFMlKAG090Dcpd8jWzIC1M315e0RH/gmOZ8Y2tZtiZnxkIwspkUg0TuBvcPxLtVeR75wGu4xUJE2RrER1NVLSh+QutLQUZj/Shu3ze5NvpNbi+vb3f5j4/QliU3MaU1KJRKJJLv4G5k4w9KMGvYxUJE2RrKRyZq2K6NTSlG0LB5NvYMHgVvmcjUrhv39eaQQBJRLJfUH8FbX2XgP5RkqQiqQpkplQI0UCoK2loGvugKdpJnP7tmHH+RuciUpuYAElEonGyc9Wq1q06Njgl5KKpClSYtqqKab2kBrNvH5taWGizwc7L6sRXRKJ5MElIQQQYO3a4JeSiqQpkpUIzaxqPt5MVSTN9HVY9FgHzkSl8M2hcHacv8GvftdJzcpvOFklkoeJ3AxYNRSiTmpaErgVpG4bYUXSsIYzSf1TmA85KbVbkZjZQ24a5KQytqs9a45H8snfwaWHP90TzIdj3BnU0aYBBJZIGpibF9Snb/dxmpYEIg5C1Am11bVjD83KEn8FtHTAok2DX0oqkqZGdrF/o1aKpJW6TY1B28aMdbN8CY/PxNxIl9TsfN7eHsisn/15qps97z3phoGudv3LLZE0FIc/gSs7QM8YOgzVrCyhe9VtyG7IzwFdg8a79pm1kBIFA99W398KAksXtRJ4A9MkTFuKonRQFOXcHa80RVEWKopioSjKXkVRQou35pqWtQzHv4bdb6jlCeqL0qz22igSB3VbnJRoaayPr7MF7WxM6OZkwY4FfZg/oC2/+kcz5cdTJGbk1p+8EklDkxCqbrfP02zirRAQth+MbSEvA8L3N+71T3wDR5dBdor6Pj4IWjS8fwSaiCIRQgQLIbyEEF5AVyAL2Aa8DuwXQrQD9he/vz9ICIW978DJb+Db3nD1sBpBEXUSUq7Xfd5KstqrxLR4RZJW8ZdMT0eLRUNc+frpLlyMSWX0N8cJu5VR4VhJ3RFCyCCH+qawAJLCoePj6s9bZqnmX00QH6R+x/otAkNzuPx74107K0k1ZRUVQNg+yMuC5Eiwbnj/CDQRRXIXg4BwIcQ14Eng5+L9PwOjNCbV3ex/D3QNYcL/1MTBnx+HL73UwmkbJtR93hJFUhtnu4ktKNrVPq2N9GjJxrk9yMorYMw3xzgellB3OSXlmLN3Di8ceIG8QllAs95IuQaFedB+KDy+HK6fhIA1mpGlxKzVfhi4joDgv6CgkVb3USfUraIFQbtuR2zJFUmlTAQ2Fv9sI4S4CVC8tdaYVHcS7a/abHs9rz4pzTsGIz6HJ1eA2zj1yaWu1Xiz6mDa0tJWVyU1KCfv7WjOtud6Y2NqwNRVp/nV7x5WT3eRnJlHwLXkh/KpPL8on4C4AA5FH+Lfh/5Nvqaemh80SsxaVu1VZ7uNO5zfpBlZwvaBdSfVJ9lplBrgEn6wca597Tho64P7eFWO2AvqfrkiKY+iKHrAE8DmWp43V1EUf0VR/OPj4xtGuBKEgL2LoVkL6Dlf3advDD6zoMsUaPcYiEJIrmP725JeJIYWtTvPrFWN7ccOFkb89lwvera15NXfLvDRX0EUFdX+5h+fnsuuCzd55/dAhiw7TJf39zL22+P87+S1Ws/V1IlKi6KgqIDeLXtzKPoQrx15jYKiAk2L1fRJCFG3li7q1n0cxPg3fnvp3Ax1VeAySH3v3A8MzODy9nqYO11NLqyKa8fBvht0Hq0qML8fQUu3USK2oIkpEmAYcEYIUdJbNk5RFDuA4u2tik4SQnwvhOgmhOjWokWLhpNOCDjyGVw7Cv1eq7CoIi3aq9uSL0BtyUoEfbPaR2KY2UNqzVcXpga6rJ7uw+Tujqz8J5zn1p8hO6+w2vNOX03izW0XGfTZIXz+bx/zN5xhS0A01qb6LBrSgR5tLPjoryCik7NqJ38TJywlDIAXvV/kVZ9X2XttL28efZPCoup/p5IqSAxVH9qKWyrgNlbdBm5pXDkij6gmNpfB6nsdPXAdCVf+uB0gU1c2TIDlHpX7XHIz4OZ5cOwJbfqDrpH63tIFtHXv7do1pKkpkkncNmsB7ACmFf88DWhE79ZdFBbAzpfgwPvg/pTaL70iLNup27oqksyE21+a2mBmD2k3oKio+rHF6Ghr8cEoN94e0ZG/L8fy+tYLVY6PiM9gyk+n+OPcDRwtjHh9mCvbnuvF+cWPsW5Wd+YPcOHT8Z4AvLH14kNl4gpPCUdL0cLZzJlnOj3DQu+F/HX1LxYfX0yRqPnfRHIXCaGqWauE5g7qDfXilobrCFhUqL7uJGw/6DYrmzvS+0V1JfHP0nu4VhHcOKvmjv06FTZNhvTYsmOiT6tWjta9VL9s24Hq/kbIaC+hySgSRVGMgMHA1jt2fwQMVhQltPhYw5a4rIzcDNg0CQJWQ5+XYcz3lRdJ0zdW/RXx97AiqY1/pATTVlCUD5kVLtoqRVEUZve055jVR+Rc2M6uCzcrHCeE4K1tgejraLH/3/1YPcOXef3a0sXRHF3t2/9m9uZGvD68I0dCE1i+L5SwWxkU1sFs1tQISwnD3tgeAx01r2CW+yye83qO38N/5/2T7z9USrVeSQgBq3Zl97mNVf2QcZca5prb/gUbJ5bdF+0H9l1BR//2vhYdoOs08P8JEsPrdq30m5CfBY/9Hzz6rur/+NoXzqy7rSivnVCDaRx81fcdhhdfv3H8I9CEFIkQIksIYSmESL1jX6IQYpAQol3xNqnRBUuPgzUj1D/wyGXw6OJKy7uXYtXuHkxbCbWL2CrhrlySWhG2j5bpF3jG2J+3tl/kVlr5UvRbAqI5EZHI68NcsTatOglrsq8jfdu34Iv9oTz6+T94vbeHA0FxVZ7T1AlPCadN87L26nke85jjPoctIVv48PSHUpnUlsxE9cHK8i5F0nm0emO9WCtXas3Iz1GjoiL+uR1mXJCrKq2WXcqP7/8G6BjAvsV1u15SsQJq0R76LFQDd2zdYMcCWPuk6gu6dhzsPG6b0jsMU2Vp1zBNrCpCZrbfC/HB8L9x6s190iZoP6Rm51l1gHMb1CeK6pTO3WQlga1H7WW1cFa3CaGqU642FH8he+iGkZ1VwMyf/WhnbUJ6TgGmhjrYNzdk7clrdGttziQfx2qn09JSWD3dh9Bb6QTGpLH62FXmrz/Lprk98HRoXttPVinX06/z19W/GNFmBK2MW9XbvADR6dGcunmKUzdPcS7+HHM95jKufcUlOvIL8/cbJngAACAASURBVIlKi2Kg48Ay+xVF4fkuz5NbmMvay2vR09Lj393+jVLb/4mHlcQ7IrbupJmVat4J3AqDFoNWPT4vRx1XVwgAcYHqDfvWZXW1b+dVfryxNfReCAc/UHPIals2JVH1rZUGE1i5wLSdqvVj72L4pqeaO9L9X7fPMbKAuYdq+8nuiSazIrnvuHYcfnoMCrJh+q6aKxFQVyR56eVtndUhRN1NWxZt1fDAW7Vc7udmqPHwBmboZMaybIgVt9Jy8b+WRExKNqcikvj6YBjZeYX8d4w7Wlo1uwlqaym42poyrqs9a2b4YmWix8w1fpy7nkLAtST2XY4jK6/mUU0How4ycttI1l5aS05BDnsi9/DUH0/x1dmvGLF1BG8dfYuIlIjaffY7SMhO4M+IP1l8fDFDfxvKsK3DWHJiCX5xfmgpWiw/s5y0vLQKz72Wdo0CUUDb5m3LHVMUhVe6vcIk10n8fPlnvjr7lVyZ1JSSVf3dpi1Qo7dSo1T/QX0Stl/N1QA1zB/gxjl127ICRQJq9KaJHex5u/Z+m8RwdUVj0vL2Pi0tNQp0/klw7qsqsbYDajdvPSNXJLWlMB/Ob4Rd/4bmrWHKFrUDWW0oeYJKCAZTu5qfl5cJBTl1UyTaOuryOO5y7c4L/lNVlo8ugd2vMdzsGsPfGl9mSH5hEbkFRRjr1+3fqYWJPmtm+DL22+OMWnGsdH9HO1N+mtaNls0NqzxfCMHX574mNjOWT/w/4bsL35GWl4aHlQeLfBbxd+Tf/Bb6G3+E/8Egx0HMdp9NZ6vONZbvM//PWHNpDQAmeib42PgwtdNUutt1p41ZG4KTg3nqj6f46eJPvNT1pXLnh6WqT5UuzV0qnF9RFF73fZ28wjx+uPgDutq6POv5bI3le2hJCFEfjppXsAp2HaHegC9urt/iiWH71Jt33GWICQDmwM1zaqivuXPF5+gZwYC3VHPU5e2q6a2mJIarD4EVrarM7OHpX9T6Wuat6/Rx6gupSGpKYjj4r4ILv0BmPDj2gonr6xZBVapIQtVwveq4dUX9stSlPMqdWHeGq//U7pyLm9V+Jj6z1Ii066fAo6wi0dXWKuNQrwttWxiz9dlenIxIwq65ARk5Bby59SJPrjjGj1O7VWnyOhV7ipDkEN7r9R72Jvb8dPEnOlh0YIHXAnS1dfGy9mKOxxzWX1nPxisb2Re1j14tezHbfTbdbLpVaUraGbGTNZfW8ETbJ5jkOomOFh3R1ipb1NLVwpURbUaw/sp6JrlOwraZbZnjd0ZsVYaWosU7Pd8hvyifb859Q3R6NK4WrrQybkUPux4Y6RrV8Df5EJEQqpp8tCooMqpvoma7X9qutpmtjzDYlOuqE7/LM2qIbUyAuv/GObULYVUmSa+n4eS3sG8JdBhRNnw/O0UNH+74ePnzEsOqjr5SFI0rEZCmrZoTcRBOrQSH7vD0rzDtj7opEVBLluiZ1MzhnpsO3/dXY8kziiOu6uJsB7DppEaBZNUwJiEzEcIPgPtY9YvYqqtagqKBaNPCmKe7OzKggzWPe7bkt+d6oa+jxYTvT/DnxYqjxQDWXlqLhYEFw9sMx8fWh5WDV/JS15fQvePmYWFgwfNdnmfPuD0s9F5IUFIQM/+eydvH3q503rDkMN478R5dbbrybq93cbNyK6dESljQZQFFoohP/D7hQvwFrqZeLTVRhaeE42DigL62foXnlqClaPFer/cY224se6/tZanfUl48+CKPbnmUz/w/IyotqsrzHzoqiti6E/fxqv8yopYPT5URtk/dthsMrbwhIYTouAuIyhztd6KlDY+9p9a/8vux7LGD/we/TClfg6+wQB1vWfFK9n5CrkhqisdE6PgkGNdDQqOiqGammiiSq0dUc1bkEfhrkbrvXlYkoDoHnfpUP/7ydtWR5168AnHsoZbszk2vONmynmlvY8L2+b2Zu9af59afYdGQDjzXv22ZFURESgRHYo7wnNdz1d6oAYz1jJnlPovJHSfz5dkvWXd5HYMcB5VzhN/IuMFLh17CSMeIT/p+go5W1V+VVsatmNJpCqsDV7Pn2h4ABrcezKf9PiUsJYy2ZuX9IxWhraXNkl5LWNxzMam5qYQkh/BL8C+su7yONZfWYG9sT3e77hjrGpNbmEt+UT65hbnkFeapr6I8DHUM6e/QnwEOAzDTN6vRde8kKz+LvyP/Rk9bj6FOQytVnvdMYYHqvK6qXFBlD/lCqDdZtyp6kLQbrCbvBm6pNIJJCEFaXlrNfk9h+9ToR6v20Kob13W0Gb57MksNdRlWkaP9blweVYMADi8Fr0lqYcecNDXwBtTvZXOH2+NTo1T/h0XN/nc0iVQkNUXfWH3VF1bt1YrA1RF+QE108nhKjdSAuisSm07qNq6GiuTiFrXom42b+t7BF0SR6mRsJOeelbE+G+b04NUtF/jk72DC4zP4cIw7+jrqze2rgJ/Q09JjQofaFcI00DHgpa4vceLGCf576r90t+tOM91mCCHYHradj/0+Vn0vg76mhVHNHh5e8n6JQY6DSM1NxT/On9WBq/n2/LdEpUXxqGPtQjEVRaG5QXN87XzxtfMlNjOW/VH7OXnjJHsi91AgCtDT1kNPS0/dauuhr62PnpYeV1Ovsj9qPzqKDqb6purv0dCKz/t/TmvTys0gUWlR/BL8C9vCtpGep7Y++PHij7zS7RV6t+pdK/lrxOXt8Nuse5vD1q3yYzr60Olx1bw1cpmarHcXay+v5cszX7Jv/D7MDaroQlGQp65s3MepD4KtvIkvzhX73bgZwypztN/N4PdhZR+1AsZjH6im8rziStu3LpcN2kksDg6p4Yrkg52XuRCTypoZPhjp3b61CyEIictg7+VY+rW3xt2+9g8X1SEViaawaqc67at7ug/fD86PwPBPVPts1Mm6m7ZM7NSnoLjA6semXFefFge8fdv2a+8DKHD9tPrz8a/U2mH2XesmTw0x0NXmi4letG1hzLJ9wUQkR9Oj8y22hf1GlhJFa91BWBjU3syoq6XL4p6LmfrXVL488yXeNt5suLKBM7fO0M2mGx/0+aBWYcOKouDZQs3cf6TVI8RlxrHy/EqACiO2aoNtM1smd5zM5I6Tqx0rhOBS4iUORB0gJTcFBYW91/Yyd89cfh72cxkfTpEo4mjMUTYGbeRYzDG0FW0Gtx7MRNeJJGQnsCxgGfP2zWNkm5G82f1NTPTqcSVakqQ3828176PiT1P5+dq6YOtZ9TXcxsHZ/0HI39C5bHHwW1m3WHFuBXlFeUSkRtDVoIr/4+jTaqSlS/EDgYEZGc0dgDxOGhqQaGBKjR7vbN3AazKc+g58ZsPp76Glt2q2vnWldFhkQiZX/jnCMODD03m0T4hmVJdWaFcSFXkjJZs1xyMpKBK8svk8K572BuB/J6/xw5GrRCWpIcsGutpSkTxQ3Olwb+Vd8Zikq5AUAd3nqV+aSZvUcgkGdfxHUBTVvHWrBpFbgb+pW/ext/cZmIFNZ7Xmz4VNqmy3LsOEdXWTpwak5aURmBBIYEIgYVoXsXO/QHhBEuHhQF5LmhdOIDLOi6y8gjJPYSUIIap0pntZe/FUh6fYELSBDUEbaGXcije7v8mEDhPQUuruQlQUhcU9FxOaEkpocmilEVsNgaIouFm54WZ1+2l9TPsxzPp7FnP3zuWt7m+RW5hLeEo4m0M2cz39OlaGVjzr+Szj2o8rswLr79CfHy/+yHcXvuPsrbMs7rmYHnY96ifXJfU6GNs0bEta577QzFoNGrlLkSwPWE5OgZpcez39Ol1tqlAkoXvVtrXOfUt3ZVg6Q3YwhYrCnqi9THKdVDOZBr6lfr82TFDN26NWqua3YkVyITqFGav9WJh/hQwtIzZeyiYt4DzfH47gtWEdGNDButzvf/WxqwhgZm9nVh27ykd/BRESl87B4Hh8nSz4V782PNrRBptqkoXrilQkmsK62My062V1uev8SPkx4QfUbdviiqKGze/dpGTTSbXJFhVVnah1cQu06la+eqiDrxq9ZmqvrkqiTtYtsbIKcgtz+cTvE07dPEVkWmTpfidTJwa27oO5dlsy0xx4oU9fguMymPj9SXYHxjLG27507PnrKXy6J5gT4Yk83d2RFwe1w9K4Yh/Ki94vYqhjiI+tD71b9q43n4CRrhFfD/yav67+RTvzKpzCjUBny858PfBr5u2bx+w9s0v3e1t780KXFxjkOKhMcEIJetp6POf1HL1a9uL1I68zd+9cnM2cGdtuLKNcRtXJB1NKWsztpmsNhZa2WjLF/yc1OspQjf47H3+ePyL+YHrn6ay9vJbr6dUUNA3br9bwMjAt3ZVh1hKyg7HS0ufPiD9rrkhMW6q5JUc+Vc3UnUer+V1Xj3A4KJZ5G85h0UyPsS1zMCpoz/m5Q/jzYiyf/B3EzDX+9GhjwRvDOpZGMqbl5LPx9HVGuNvxn5EdSc7K47vDEejpaPHuE52Z2rN1gye5SkWiKSzbwujvYN+78PNIdck8aLFa6qCE8ANg5qiOrS+sO6k22dSoyvNfbgVB3EUY+nH5Yz2eU//5ey5Qn6p2vayWxK/HctVfn/2aX4J/ob99f55o+wRuVm50tuqMqZ5pubHdnfVwtDBis380Y7ztKSwSvLrlAr+dicbcSJchbrasPxXFtjMxLBjowvTeTqX+lRJM9Ez4d7d/15v8d9LSuCWz3O/RD1BPdLPtxrYnthGdEY2xrjEWhhY1Nt15WXux7clt7L66m62hW/nU/1NWnl/JJNdJTHSdSAvDFrW/WaXG3K6G3ZC4j4NT30LQTugyBSEES08vxdrQmmc9n2Xvtb1VK5K0m+r34dElZXZnmLWEWBht15sfYg4QnR6NvYl9hVOUo89CuPAreE9V+7pbd4LCXD7c8CeOFi6snemL0aqFYO+DoiiM8LDjsc42bDwdxRf7QnlyxTFGeNjx6pAO/BUYS0ZuAXP7tkFRFD4c446zVTOGdLalg23DB8WAVCSaxXMidHpStZMe+Ry+e0SNkBrwlppsdPUwuI2p16d9bIojt+IuV65IAreo2bsVJU5ZtYOBxSGzjj3VbdSpelMkZ+LO8POlnxnffjzv9Hyn2vGKojC+qz2f7Q3helIWG05H8duZaP7Vrw0LBrhgYqBL2K10/vtnEB/+FcT/Tl3jtaGujHC3eyhLkTiYOuBg6lD9wAow1DFkdLvRjG43muCkYL6/8D0/XvyRHy7+gImuCfYm9jiYOOBo6oiDiUPpCyAjLwN9Hf3S9wih1n1rO7CKK9YTrbqq/+sXN0OXKVxIuMCFhAv8p8d/MNI1wsHEgej0KmrQlfRedykbMJGhZ4QWWoztvogfth5gd+RuZrvPrmCCCtA3gRfPl1oFRIuOKEA7rvPqtIlYGylqoqHn7eKQutpaTO3pxOgurfjhcAQ/HLnK34GxGOhq06utJW6t1NWhga42Lwxq3BWwVCSaRtdQLTftPQ2OLYeTK9Uok3aPqQ1q6vuLVtIx7dYlcB1e/rgQ6hfOuR+Y2FQ9VwtX1W8SdUINZ6wDQgj84/wx1TPF2siat46+RUvjlrzS7ZUazzG2qz2f7wvh5V/P4ReZzCRfR94YdrvyqYu1Caum+3A0NIEPdl1mwYazrHK8ytsjO+HtWEWkjqRSOlh04LP+nxGRGsGR6CNcT79OdHo0QUlBHIg6QIGouLzNUKehLOy6kFbaRpCfqT4wNTSKojrdj34O6XH8GvwrzXSbMaLNCAAcTBxKQ7YrJGwfGNvejl4sJiM/g2Z6zWhlYk8X6y78HvY7MzrPqLlp9A7T8s4bJowQCjNcsrE3N1KtAogKI7ZMDHR5+bEOTOnRmuX7Q/ktIJoFAzWbayIVyf2CYXN16ez7L/jnYzizVo1kucO5Vy/om6hZ8nGXiUyN5N///JsPen9AR8viG29MgBqf3/fV6ufS0gJ7XzXbvY78efVPXj/yeul7BYXVQ1fXKpO7ZXND+rhYcSQ0gW6tzXn3iYrLn/RpZ8WuFx5hS8B1Pt0TwphvjjN/QFsWDWm8vg0PGm3M2tDGrOxqtKCogNjMWKKOfEzMld9QHHth3G02IckhrLu8jv1R+3m/878YAWrnzsbAfTwc+ZTUCxv5O/JvRrmMopluM0BVJKm5qaTlpZU3nxYWqCZm18fLWQYy8zMx0VVNR5M7TuaVf17hz6t/8njbCjLUqyAhI5d3/oqgm44tngbFibfxxRFcVeSQWJsa8N/R7vzfKDeNr66lIrnfMLWDx5er/d4z4tRw3frGuhP58Vd47chrhCSHcDr29G1FcnGLWr+o48iazeXYAw7sVbPlq8n0T8lJIeBWAH3t+6KrpUtmfiaf+X9GJ8tOzHCbwdWUq9ib2FcdPVMJ8we4UCQEyyZ4oadTeRCBtpbCBB9HRnq05I2tF/n2UDhjve1p06Iec4QecnS0dLAPPYD96bWqiTQlGZyHMtR5KE91eIoXDrzA1yEbGQZomTbCigTUMiM2buwI/oVc7VzGt79d5qfE3HY9/TqdLe96CIkJgJzUChMaM/LUFQmoyacdLTqy4twKhjoNrTBwoSL8I5N4cdM5MvMKMXHxQKskBPj0j2q4flV5MsVoWomALJFy/2LZVu141hBYtGVFYTyXEy+jo+hwLa24h3pRoepAb/9YzUOMS0I3r1dcZVUIwdlbZ3njyBsM2jyIhQcXsvDgQrILsvnuwnfEZ8fzVve3GOo0lGe9nq3101wJPdpYsn52D6xNahbe2Exfh/+M7IS+jjZf7g+t0zUllRC2D/5YqJplu80sLrOudhS0bWbLDLcZROcmctLQoHFMW8UI18f5tTAZL0t3Olh0KN1/pyIpR9g+VRm26V/uUEZ+Bsa66gOIlqLF812eJyYjhq2hW8uNvZvM3AKW7wthwvcn0dZS2Pyvnhg7uKv9RyL+Udt193qhbKOs+xi5InkIOa2vwyoTQ8a2HkZoVsztGk5XD6sdFN3HVz3BnbT0VuPrr5+EDkNLd2fkZbAzYie/hvxKaHIoxrrGjG0/Fmsja7488yUzd88kKDmIUS6j8GhRh/4q9UALE32m9mrN94cjWDDQBRfrxolweaC5eQF+nab64sb/rOYc+f2o5kRZqXb8QY6DMNfSY7OJCb2MrRtNtCN6EKmny39blg21L1EkFTrcw/aqYe4VWAYy8jOwNLidhtinVR+8rb1Lq08fjj5MeOrdnREV8gqKyM0vpEgouHYcxIZxizEz1Cc/qT0/mDaj4+4XGGBkpXZXbCLIFclDxqWESyyM2U3r/AJedRyGk6nT7VyNi1tA31R19NcUPSO1oU+U6ieJSI1gyfElDNw8kP879X/oKDos6bmE/eP382b3N5ntPpul/ZYSlByEobYhL3q/WP8fshb8q29bjHS1WbZPrkrumdRo2PCUupqdvFnNubB1V4/FXSwdpqetx5Palhw0MiQ+p3Gamm4L3cZLEb9gn5/PYMOyfhkjXSMsDSzLr0gy4tUEYJfBFc6ZmZ9ZuiIB1cQ0s9NzxGfH8+XZL8krymO483CeaPsEQxxH4qD7COkJHqQneNBCqxddbbyJKvqDj/3fJTYzllmRm/nW3IyXjQo55vkkFJvNaktuYS5vHnmzUYt8yhVJEyO/ML/G9te7uZRwiTl752CqZ8J3kWEYpd7A0cSRHeE7yM5OwfDKDrWUdQU1iarEsQec/oH0rESm/jWV3IJchjkP46kOT5XJri5hqNNQHIwdKBJFWBnWsdxLPWHRTI/pvZ1YcTCcWX2SZRRXXclJhfVPqY3QZu5Wk+5AjexTtCE2sEw4+bhcWKMF28O2M8djzj1d+udLPxOZFsk7Pd4p5y/IK8zjw9MfsiVkCz2svVnqtwODtPIN5RxMHMrfeCMOqluXQRVeNz0vvdRHUsL+c83IvDofUWCGu6Un89zasuZYJOuPR5KeW8DgTjY8P9AFD/vmCCH44eIPfHX2K/66+hc6Wtq8l5DMejNTXko4yqqEwAq/P9VxKeESf0T8gbOZ8z3/bmuKVCRNiJ8v/cx3F75j5+idta4tdS3tGnP2zMFU35RVg76j5RUvSL5K6zZq292oy7/SITdNTd6qLc594cTXrD3xX1JzU9k0clN5p+Vd1KaxVEMzt29bfj93g3nrAtixoA+2Zg1TRuKBpSAPfp2qNmqbvLmsg1jXQC0HdFd9t9apsXS3MuXXkF+xMrTC3MAcCwOL0ldNo/ZWBa5iWcAyQM3Sv9PHFpsZy0sHXyIwMZDZ7rNZ4Dkf7YBWkHKt3DwOJg6cjr3Lzxe6F4ysKm6hS9moLYDLN9L438lrTPLtja62Fj8evcqq4tIlw9xsWTCgHZ1a3o4KUxSFuR5zaWHYgm1h23ir+1t0OPYtfaza8MyNP3lu33NsGrmJlsYtK7h65YSlqI3UgpODa3XevSAVSRMhLjOOFedWkF2QzW8hv9XqSUMIwbsn3gUFVg1Zpf5jmtlD0lWcvFTFcS3sbzoYWYFTHcKN2/Qn2cCUddH7Gdx6cLVK5H7DzFCXn6b5MOabY8xZ68+v/+qJoV4DlU6/Xygqqrq6QU0RAv54ESIOwZPfVJz3ZOumtqa+89ppN5jSdgwvJp/inePlE08NdQzLKJYyL0N1G5QUxLKAZQxxGsKNjBt85v8Z/R36Y6JnwsmbJ3n1n1fJK8pjef/lDGpdvKows1drfN2Fg4kDOyN2ciTsBpZGxpjoa9EybB+5rQeSkpYLqNG/CgqKAsb6CrmFuaUhxEIIFu8IpLmRHq8OccXMSBf3Vmb4RSYxq48z7Wwq97+VJHkC8PhyWgArU59g0q5JLPpnEWuGrqmVFSI0WTXTBidJRSK5iy/OfEFBUQEdLTqyKXgT092mo6tVs3+ubWHb8Iv1Y0nPJbefbizaQPJVHE3UNqVRKeFq0y7tOvxL6Oiz2rEjWXk3eM59bu3Pvw/oYGvCl5O6MHutP29vD+Szp6qpKtvUCd6lNlOa+nvNunRWxslv4fwG6PcadKmkMrGNm5rkWhIinhkPRfn0t/Hh5LAvSMpJIjknmaScJBKzE0nKSSrzisuK40riFZJyksolOva378+Hj3xISFIIk3ZNYsW5FbQwbMGXZ7/E2dSZZQOWle1M2dyxfAMpwMqgJQLBtLV/U5RnjYcSzg79JN64YM3v5w6U/0jNC8BO7W+Tk1/IT0ev4heZzEdj3DEzUr+XY7vaM7Zr3aLSnMycWNJrCa/88wrLzyxnkY/ai6hIFBGTHkNIcgghKSGEJocSlxXHR30+Kq1YULIiuZZ2jaz8rEbprikVSROgpMDcbPfZdLHuwvz989l/bT9DnYdWe25CdgKf+n9KV5uut596QO0vfXk7RrpGWBtaEZkeCa2q6fJWCfFZ8WwsTGBEZhYuKTfAsmkm+A3qaMOs4uqpi4Z0eLBNXDFn1O3fb8O//qm4XW115GWphQfbDID+b1Q+rtThHqiaQdOKo6PM7DHUMaSVcasa1fwqaUJVomByCnLwtfVFV0uXzladGd9+POuvrAdgiNMQ3uv1XvmbqJmDGll2F6dDVN/K3EGmeFl6Y3/hOCJEYcDwCfTSt0AItaC9EFBYVMTPfmfIAtYejeXTzftJzsqnt4slT3WrW/mZihjiNAT/WH/WXl5LfFY8MRkxhKaEkl2QDairI3sTe66nX2d/1H6mu01HCEFYShhWhlYkZCcQlhLWKFGRUpHc59xZYG6O+xwMdAxwMHFg/ZX11SqSK4lX+O+p/5JbkMvinovLlkW3cIbsZMhOwVHPnCjd6OrbhVbC7sjd5BTl86+MPLi8o3HqJzUQz/RszY9Hr/Kr//U61SsqLBLsDoxlzfGrPOnViik9NN9Pu0JuXQEtXTWa6sIvak/x2nJuPWQlQt9FVdeDK1EkscWKJLVYkdSy8q+iKJjpm2Gmb1Z2lVHMC94vEJ4azkCHgTzT6ZmKE/WaO6jtd/Oy1IhDIDY1hz8CctFtA/YtshnayQ5O+UHLLozqU7F/pItLFpP+hPg0BV9nC6b2dKJXW8t6Tw5c5LOI4ORgTtw8QTvzdoxpN4Z2zdvR3rw9bZu3xUjXiBFbR3Dm1hmmM53EnERSclN4ptMzrLu8jqCkIKlI7kRRlObAj4Ab6sPBTGAIMAeILx72phDiT81I2DBEpUdxIeECr/m8Vvp09bTr03zs9zGXEi5V6LSOSI1gxdkV7Lm2B1M9U5b0WlL+i2de/D75Kq2LtDioq6vmhNSB0ORQLAwscGrTDa78ASM+q9sT7p0UFd77HHWgtWUzertY8ovfdRYMcEGrkkZCJQghOBQST9DNdG6kZHMsPIGI+Ez0tLW4dCONxzrZYN1APSDuiVuX1OoFyddg//vQaVTpjbVGFBbA8S/VEjnVJc4aW6s9QUoc7qkx6raekxHN9M1YM3RN1YOaFyv21OjSysPL94VQWGCEqY4RgYmBiMxElGg/eKTyem/ZhZkAfPt0b7rbdasP8StET1uPtcPWVjmmi3UX/on+ByFEqX+kn30/toVuIyS5Bu2864GmlEfyBbBbCOEKeAIl7cSWCSG8il8PlBIBOH5DdVL2tb/tBC+pE/TO8XdIyE4o3R+TEcPbR99m9O+jORpzlLkec/lr7F8VZ4tbFCuSpKu0zs4gSVubNJ26PVeEpYTRrnk76PSE+rR3p2O1LqTdhI+dYNPk20+vjchEH0diUrI5EpZQ5biTEYmMWnGMGav9+Hh3EDvO38DCSI8VT3uze+Ej5BcW8emexnN41pjcdLWyrE1nGPJ/kH4DTq6o3RyXtqlz9HmpZtWpbd0gtjiXJC0GdAwbpvxPdZgVm55S1FDfsFvp/Op/nSk9nBjuPIxdEbt4a//z5CDUnu+VkJmvKpI780g0hbeNNym5KVxNu1rqH2lnrq5aGsvh3iRWJIqimAJ9gekAQog8IO9+qDHT0By/cZxWxq1ul99GdfB93u9zFh5ayDN/PsOHj3zIn1f/+OmP7AAAIABJREFUZHPIZrTQYkrHKcxyn1V1iPCdK5KUGDBWe3bXNm69SBQRlhLGmHZj1MQtHUM48D6M+b7uEUFBO9XKx6F71WiggW+D79xGW6E81tkGcyNdNp2Ool/78v3aQ+PS+Xh3EPuu3MLOzICl4zwY7m6HsX7Zr9P0Xk78ePQq03o50bll/bc3rTPxxTcX687qasJ1JBxdrlagrkmmuRBqpeoWrtC+ej8doDrcT62Em+fVqCkz+/ptj1BTmhd/j1JVRfL53hCM9HR4fmA7mhu9g00zG7499w1h9g6sse5EZWu0jHy1z3pJ1JYm6WKtmqTPxp0lLCWsNLrN1cKVbWHbKBJF99TtsyY0lRVJG1Tz1WpFUc4qivKjoiglf8EFiqJcUBRllaIoD1Q2WX5RPqdvnqZXy17lbK+9WvXix8d+JD0/nWf+eobNwZsZ7TKaXWN2schnUfV5JvrGqrkhOoDWKTcAynQjrCk3Mm6QXZCttpLVN4aRn0PcJfimp9rTvbDicuL8/RbsW1LxsaBdYNkOFpxWkx13vw4/DIQb52otX13Q19FmrLc9ey/HEZOSXbo/Li2HN7ZeYMjyw5yKSOK1oa4cfKU/T3VzKKdEABYMbIe5kR7v77yMEFX0Hm9s4i6p25KWAo++CwU5cOjDmp0f7aeaqXouqLrL5p14TQZDC/XvGH6o8ar+3o2JnVrSJ+U6gTGp/Hkxlpl9nLFopoeWosWzDkP5+FYCV3TgQMyhSqfJyFMVibGe5lckTqZOmOubc+bWGcKSi60DqKX+swuyq+61Uk80FUWiA3gD3wohugCZwOvAt0BbwAu4CXxW0cmKosxVFMVfURT/+Pj4iobUij/C/2BT0CZSc1Pvea6quBB/gayCLHq1rNgG7dHCg7XD1jLLbRY7Ru3gnZ7vYNvMtuYXsHCGsL045BegoNSppELJUrq0J7nX0zD/lNrPZM/b8MMAtczEnVz5A058rZZkuZvsZIg8Aq4j1BXN5C0wbhWk3VDn2v2mmj3dwEzq7oiWojDos0P8Z3sgn/4dTP9PDrElIJrpvZz559UBPNu/LQa6la+SzAx1eXFQO05GJHH6auOUAqkRt66ArtFtf4GVC3SbBQE/F/fBqIZL29QK0Z2erPk1rV3huRPQeQzkpt57/kpd0dJWnfwpUXy+NwQzQ11m9bnDf3huPUMys7E2sGT/tf2VTlOyIrkfTFuKotDFugsBcQGEpYThYv7/7Z13eFzVtbffPaNebFnFtmRbcq+4g+VuXMC0AA6da1owYAiEkgsmBJKLKRffBC4k1IR8CRDACaYEuMaAC+6VuBdkuTdZstxUbLXZ3x/7jDWSRtL02SP2+zzzzMw5Z878tGY06+y1115L/S86C1PuOO7BZ+onkeJIDgIHpZTOxhdzgCFSyqNSyhoppQP4MzDM3YullH+SUp4vpTw/I6NhqMIbahw1PLPqGZ5b/RwTP5rIjCUzWH1kNQ7p8Ou87lhxeAU2YWNYpts/C1D9IB4a+pBvXe/adIGaSmIQZCW292lE0sCRgApb3PQhXP+uKoX/5wm1DqDsmKoMCyrEUXWm7gl3fguOahVuAasp0TVw/1oYeruK5b+WCytfU22KP57WaOVhn9izFLZ9TreMJOY+OJorB2bxj7UHeHVRPpP6tmPBIxfym5/0JTUxxqPTXX9+J1rHR/PuqoarqcNG4TYVlnIdTYyboWo7zf9t0691OFTjte6T6vQv94iEVLjmz3Dnt3DhE97rDhQp2ZQW7mHhjkLuHtuV1vHWeixHDWz4AFv3iUzIuYhlh5adS7WtT1lVGVG2KGLtelTnHdJuCIdKD1FeXX7uf7F7Snfswh6SFe4RMUcipSwQQhwQQvSSUv4ATAS2CSEypZRWJximAFsaP0tgOFh6kDPVZ7ij3x2UV5czd/dc5u6ZS4ekDlzd/Wqu7n61d6OCJlh1eBX90/u77VUeEJwT7uk9yGndhb2n9np9ip0ndpKZmNlwiC+EumLtMg4WPK0cwPbP1YKwitMqZXTJ7+D47tr2v6BGK0ntVXtUV+JT4Ir/hQE3wpcPwddPqBCFo0ZN2nZq3Nl6jHOVdnkx9L6c7m2T+Z9rB/Kfk3tRXlFD53Tv4+HxMXZuuKAT/2/ZHgpOndVjbUrh9oaFORPTYMwvlSPZvRi6jnP/2oNr1eR8v6d9f/9AfFb+kJJN5f55pCXGcPvIzrXbdy1SiQCTn+ei1Cxm/zCb5YeWMymnYS+SksoSkqKTtOgFArXzJFB7URdrj6Vzq87kHQ9+5lakjEgAHgDeF0JsQoWyngf+Rwix2do2Hng42CKc6XWTO0/myeFPsvD6hbww5gU6JnXktQ2vcfGci7l/wf1+h71OVZxiS/GWRsNaAcE54Z41mKHthrL9+HbWHPHu6j7/ZH7d0Uh9nA7gjnkqnLJvuepJ3+dKtf+YS9XdqjOQv0C1AG4s9p6dC/cshQc3wa8LVCvS0kKvNDfKgTWqH8TZk1BQu2itbXKcT07EydTcHGqk5IPVGoxKyo6pVgHt+jbclzsdWmfDN79WIw93OMNank6ya8gR0kmpOc69YzqR6Dq3tf49SEiDXpcxpN0QUmJTmL9/vttzlFWVaTHR7qRPah/i7OoixfX/sWdqz5CkAEeMI5FSbrDCUwOklFdLKU9IKW+RUva3tl3pMjoJGnkn8rAJG11TVHvRuKg4Lu96OW9PfpuvfvoVdw24i6WHlvLWprd8fo8aRw1f7v4Sh3QwImtEoKQ3JNVqkZo1hFv73krHpI48s+oZKmoqPHp5laOKPaf2nIvJNknOCJi+FG77UjXscb53cX7tMbsXqz7evS9v+lz2KGiTA/ZoSMxQP46BYMP7EBVXqyVAZKclMKFXWz5Ys5+K6pqAndcnCrep+7Z9Gu6LjoNJv1Vpupv+0XC/wwHbPlNpsd6GtTTi60Mx2ITkpt4u81tSqgzB3pdDVAxRtijGdxrP4gOLqaqpanCO0spSkmP06V8TbY+mf0Z/shKz6kQHshKzKCwvDEro3ZWIcSS6kHcij+zkbOKjGpZa75jckQcGP8BV3a5i9o7ZXmVLVDmqWHF4BTNXzmTiRxN5YY0a5fhSRtpjsgarWPWA64mLiuOp4U+x9/Re/rL5Lx69/MDpA1Q5qs5liTRLVCx0GaNGG7FJkJwFxS6Nf36Yq/qheFM4MjFd1W7yl6oz6mq779WQ0Uf9qASQW0d25lhpJW8t3s3eY2XUOMKUxeVs5drWzYgE1GR41hCVwl1ZXnffwTVQckTZKELJLyzl60Nqfiux/FDtjpICNRJtX7sKfFLOJEqrSlldsLr+aSitKtVqRALwxLAneGHsC3W2pcenUy2rOV1xOqjvHRFzJDqx88TOOm063XHfoPv4as9X/HH9H5k1dlajx1XUVLDi0Arm75/Pdwe+43TlaeKj4hnTYQyTciad620eNOxRcOGMc09HdhjJZV0u4+3Nb3Npl0vdlqFwZedJFZZqMrTVFGnd6o5I9q2AnFEQ5dlENqDWPexd6tv7u7Lj/9TczaCbVTju+3eg6qy6Sg8AY7qn079Da176No+Xvs0jNspGt4wkerZLoke7ZHq0TaJnu2Q6pSZgb2Y1vV8c3armlJLaud9vs8HFz8LfLoNVr8NYl9XdWz9TYa1ekRvW+tOSXRTZrbUyrsUbnSO1jNo6ccMzh5MYncj8ffMZ3WF0nfOUVZXRNiF03R09wV1kIC1edXA8duYYKXEpQXtv40i8oLyqnAMlB5rtK94+sT1T+07l7c1vc1u/2+ib1vDqr6i8iOu+uI7is8UkxyQzvtN4JmZPZGTWSOKiwjch++gFj7L00FKeWfUMf7n4L01OJu46uQubsDXrcBolrbsKlQCUFUPxTu9rPiVmqJThmioV6vKVjR+qVc+dx0BVuVo8d3CNqg0VAGw2wUfTR7DtyGnyj5aSd7SEnYWlrNlznM82HD53nNPBjOyWxj3jupGRHOCsoMLtajTS1CRx51GqIvDG2XUdyYFVao4qVp+QjjcUnDrLp+sPMfX8gbDJdm51O+AyUqsN+cXYYxjbcSwL9y/kqeFPYXdZEFtSWXIuvK0zzsZxxWeL6Y6PF3weYByJF+SfzEci6dGm+VDOz877GXPy5jBjyQyeHP4kuZm5dfbPyZtD8dliXhn/CmM6jPG562GgSY9P5+GhDzNz5Uw+3/U5V3VvfK1A/sl8spOzfXd86T2UEyg/rn60QS1A9IZEq8NieTEk+5gtV3IUdi2E0Y+oK/KcUaqr3+7FAXMkAHHRdoZkt2nQhbHkbBX5haXsPFrKzsISdhSU8NcVe3l/9X5uH9WZ+8d3rzsp7CuVZWpl+QV3Nn9szmhY9BycPa3mQ6or1Wgmd7r/OsJAcWkF977/PVLCz8b1goNd6zbbKtquLkoS63bsnJQ9ia/2fMW/C//NBe0vOLe9fptdXXH2lHctpRQMzByJFzgztnq26dnssckxyfx+3O+pclQx7ZtpPPLdI5yuVHHKKkcVc/LmMKrDKCZkT9DGiTi5psc1DMoYxO/X/Z4TZ0/U2Zd/Ip+nVz7Nb1f8lrUFa30Pa4EakYAKbx1YrdJ5va1AnGiFF/zJ3Dr8b5AO6DlZPY9rBR2GwJ7ATbg3RXJcNIOz23D9BZ349eV9ee/OXOY/Mo6L+7XjzcW7uOaNFewvLm/+RM2xezHUVDRM/XVH1mBA1mavFW2HmkrIcl8NV2d2F5Uy5fUVbDt8mldvHkKn1ATVe+fAajXJDtZIrWECwugOo4m1xzJ/X93srdKq0shwJFZoq/hMcVDfxzgSL8g7kUdCVIJHvRMAcjNz+eyqz7h/0P0s2r+I51Y9B8DiA4spPFPIDT1vCKZcn7EJG78Z8RtKK0t56fuX6ux7Yc0L/Cv/Xyw7tIxYe2xt5zlfcHUk+1dD5kDv+8UnWgtM/Zlwd87TpLk4xa4XwqHvVS/yMNAlPZFXbhzMO3cM48ips/zk1WUszvMzqSDvK5XMkDOq+WOdDsNZlcBZnqaRtrO6smbPcX76xgrKKqqZffdwLjnPGrV2ylWj2OJ8lY1WuMNtAkJCdAKjskYxf//8c5lPFTUVVDmqtCiP0hytYloRbYvm2FkzItGGvBN5dG/T3asCaHFRcdwz8B7uHng3c/fMZd7eecz+YTaZiZl1KvrqRo82Pbit3218lv8ZawvWArC+cD2rC1bz4JAHWXDdAuZfN58rul7h+5ukZKtRSOE2NSro5GVYC1wciR//KMX5qg5Ugkt9si7j1Chl30rfzxsAxvbM4PP7R5HZOo7b/7qG3329g+oaH1I5HQ7I+0b1ivEkmSExXa0pcTqSIxshtnVt2nYE8MXGw0x9ezWpiTF8et8oBruGFLOttPr9q6wKC2V1JtpdmZQzicLyQrYeUzXKnHW2dMvacocQgrT4NDMi0QUpJTtP7vQorOWOaf2ncV7aecxcMZPVR1Zzbc9r60ze6cg9A++hQ1IHZq6cSWVNJW9tfIvUuFSu63ldYN7AHq0WRW79TBUN9GXFc5LTkfgR2ireVXc0ApBhZea56e8danLSEvnkvpFcP7QTry3axQ1/WsWOAi/TOY9sgNIC7xYSZg1ycSQbIHNAeCr2eomUkje+28UDH65nUKcUPrl3JNlp9er4pvdQFw8HVjWbEj2241iiRBTf7v8W0KuEvCekxRlHog2F5YWcqjjlsyOJtkXz3JjnqHRUEmWLUmXXNSc+Kp4nhz/J3tN7mbFkBssPL+e2frcFtgd0WvfaH+tOuU0f647YVmCP8TO05caRxKcCInCLHf0kISaKWdcO4A83DSavoIRLXl7Kz/62lrV7PSwGmTcPEJ7NjzjJGqxK2JQdU90NM/XsY19V4+BEWSUA1TUOnvh0C7Pm7eDKgVm8N20YKQluRmBCqO/b/tUuizTdj0hax7YmNzOXBfsWIKWkpKoEiBxHkh6fTvHZ4DoSk7XlIc4yAx4vvnND19ZdeenClyg+U3wuLU93RncYzSWdL2He3nm0jm3NDb0CPK+T1k3dp2RDq0zvXy+Ef6vbK8tU7ai0eiEbe5RabxGIxY4B5MqBWYzrkcG7K/fy1xV7ue7NlZyf04Z7L+zG+F5tG+/omDdPjfgS0zx/M2fiw8bZapLex1bMwaTGIbnr3XV890MRma3jSI6LIu9oKT8f341fXtSr6Q6X2blq3mjvMlUROK7xnjETcyYyc+VMdp7cSVmlNSKJgDkSUBPuW4u3BvU9zIjEQ1w7j/nD2I5jmdJjSiAkhYzHLniMzMRM7ht4X+Djws6RgC+jESeJGb5nbR3fXVdHnfOmq46P/lBRqvrYB5DWCdE8MLEHy2dM4Okr+3Hk1FnufGcdl76ylE/XH6Sq/hzK6cNqjsPb+ljOCffv/6ru6020V9c4eOqzLfzkj8vYcig8SQl/XLiT734o4qZh2VzQOZXYKDuzrunPo5N7N9sm+dyc3K6F7kvGuDC+03gEgvn75mtVQt4T0uLSOHH2BDWO4JXnMSMSD7m1761MyJ5A61iNOt2FiIyEDOZdMy84XdbSLcfsryPxdeTgLmOrznn9DAms/bNq4PXgJlUfLIDEx9i5bWRnbs7N5stNh3nju108/I+NvLown4/vHVkb0tn5jbr31pHEt1FzWMX5EJNcZ6K9vLKa+z9Yz8IdhbSKi2LK68t5dHIvpo3u2vwPuMWp8irW7D1OlE0QG2UjNtpGbJSdOOs+Nsq6j7YRY7c1OO/ivCJeWbCTnw7pwPNTzvO+Em/WYBUWralsdKLdSXp8OoPbDmb+/vl0TFa95iPGkcSnUSNrOFlx8lw6cKAxjsRD7DY7Oa0C+0MQSQStVWenXFUNuL8fE/iJGbVxbm9xOhJ32UgJabVtaX3FWbOr5EjAHYmTaLuNKYM7ctXADny9tYBfzF7PjI838ebUoQghKNr0DXZ7Gg9+cZrY6HXcOiKHsW5aCLslazCc2AOZAyivdrAk7yg7Ckr4eutRfig4zXNTzuOy8zJ5/JNNPD93B4vzinjxukFNlsvffPAUf1uxly83Haai2vMMtBi7rY7DKS6roFe7ZJ67ur9v5dyj49Tfd2B147XHXLgo5yJmrZ3FtmL1XUuM0T9rC+qubjeOxNAysUfDuMf8O0eSNSKR0vusouJdqnikux+FxHRV9t5Xqs6q9FJQDb6CjM0muLR/Jo+dOMNzc7fz99X7SY6xMWbvUlbbh1BSUcP2glLW7TvOd/95oftJ6PpkDYatn0DmIO5573uW7jyGEGqdy5tTh3JxP7Uu482pQ/nH2gM8/cU2LnllCS/8dEDtmg2LkrNVzJq3g7+v2k9ijJ1rhnbkqoFZxETZqKh2qFtVTe3j6hoqqmofn62ytlU7qKhyYLfB/eN7EB/jR/ajc2FiM6EtgInZE5m1dhZzd88FImhE4rK63ddkoeYwjsQQ+SRmqPBExekmJ0zdUryrdsLf3XnLj6vmWb6kah9cq9KaIXA9UzzgztFdWJZ/jJlfbKWrYx9Xx57mwkuu4dJho9hRcJrLXlnKi9/k8czVHlSWthqM7Y7tydKdx3hwYg+mj+vW4MdbCMGNw7K5oEsqD83ewPS/f89Nwzrx1BV9sQnBZ+sP8fL8nRwtOcsdozrzyEU9SY7ToKLDoJtV5d92zdsiMymTfmn92Fq8lRhbDDF2L4qLhpFzI5IgpgAbR2KIfJyLEkuLfHAk+Y33Hk9IB6RyJkkehoJc2bMYnCHBkgLvX+8jNpvgxesHcvVry7kr6QAUQXzP8QD0bt+KW4bn8N6qfdw0LJu+Wc30FckZCde/x7Mr00lNLOOecV2bHAF0y0ji43tH8r/z83hz8S5W7CqmrKKGY6UV9O/QmjemDqm7MDDctO2j2v96yKScSWwt3hoxGVsQmjIpJmvLEPn4Wial/DicOd7EiMSKJ/uaubV7sertkdg2JKEtV9KTYlny6HiubbNbzf+kdDq37+GLetI6Pprffr6Fk+WVTZ9ICDYmj2XhzhNMG9OFhJjmrz1jomzMuKQ3H0wbTny0nX5ZrfhgWi6f3z9KLyfiA5OyVdvdSAlrgdIaa48N6loSMyIxRD6+OpKmUn/rnNcHR1JRomp1jX4I8ueHNLTlxCZr1BzPedfU2Z6SEMPjl/ZmxsebGfrsfEZ0TWNyv3Zc3K897Vo1nCR/dVE+reOjuWW4d8kCI7qlMe8hfcsA+ULn1p3pntL9XFvbSEAIQVpcWlArABtHYoh8En0sk9JU6i9YoS18Sy3etwJkjSpDX7BZlScJNYfXq3kjN6Xwb7ggmz6ZrfhqSwFfbyngqX9t5al/bWVwdgqT+7Vncr/2nCiv5G/L9/LttqM8NKmHHnMaGvD86OepcjRsv6sz6fHpZo7EYGgSZw8Jb0cOxfmq70hKI1farr1OvGX3YtVNsFMubP5IOZNQ4yyD30hPlQEdUxjQMYXHJvdSLWi3FvD11qO88NUOXvhqBwDJsVFMG92F6eMaCf/9COmT1nyGl26kxqdyqPRQ8wf6iHEkhsjHHu1bOZPifFWapbFquP7U29qzRJXgiI5XbW1LC1UFXlsQpiWrK1QtrINr4dA6NbGf0VuV/mh3XoNmTfURQqh2v+2SuX9CDw6eKGf+tqNER9m4alAHkgLRVMsQVtLj09lUtClo5zffEEPLwNsyKeXHIX9B00UMfa23VV0BhVthzC/V86T2Ksx15nizP+oecfqwCp0dXKccx5GNKv0ZIDkTWmWp+liVJarro5d0bJPA7aN8bJ9s0BJnmZRqRzVRtsD/7BtHYmgZJLb1buSw/BU1Ie78sW/0vD7U2yrepXqZpFul6JOsLo4lBf47ktJCeGWgchxR8WrBYO490OF86HgBtLaarjkcqhhlUjv/3s/QIkiLT0MiOVlxMigFY40jMbQMEtNVT3FPKDkKq99SZVnaNVMaIyHd+3pbx1SlaDKsVcTOH/PSo4AHiwCbYu9S5USufw96XarCeu6w2aB1R//ey9BicDqPY2eOBcWRmHUkhpZBYobK2tqzFD6eBtu/aPzYpS+qH+MLH/fgvOneh7acjsSZDZYUgL7yTvYuh5gk6HVZ407EYKiHs0xKsDK3IsaRCCFShBBzhBA7hBDbhRAjhBCpQohvhRA7rfvIXu1k8J2ktqq/+jtXqCyp1W+5P+7kflUWffDUxhciuuJLaKvoB9Wm1lm/69yIJAApwPtWqEwwuwkmGDzHdUQSDCLGkQCvAPOklL2BgcB24HFggZSyB7DAem74MdJtguqz/pM/wNA71ER0tZtV24tnqXtPC0UmpNfW2/KUY3m15fEBYpPUKMLfEUlZMRRth86j/DuP4UeHs0xKsBxJRFzWCCFaAWOB2wGklJVApRDiKuBC67B3gO+AGaFXaAg7Hc+H26wGUvEpatRRsEltd3IsHzZ8CMPu9nz+IDEDr+ptORxwbCd0Hl13e1IAyqQ4KxHnGEdi8I6EqAQW37CYlNiUoJw/UkYkXYEi4K9CiPVCiLeFEIlAOynlEQDrvm04RRo0wdn5bv/Kutu/ex6iYmGMFymx3tbbOn0Qqs9Aer1y3Unt1CS/P+xbYWVqDfHvPIYfHUIIUuNSg9ZXKFIcSRQwBHhDSjkYKMOLMJYQ4m4hxDohxLqiIr16cBuCQHI71dnP2QsE1IK9LR9D7vTayW9PSPBy1XyRNdHuzpH4PSJZBp0uaHwBpcEQJiLFkRwEDkopV1vP56Acy1EhRCaAde82CC2l/JOU8nwp5fkZGT6UAzdEHtkjlCORUj1f9BzEtoZRv/DuPN4WhDxmdVTM6FV3u3N1u6+cOaGcYc7o5o81GEJMRDgSKWUBcEAI4fzvnAhsAz4HbrO23Qb8KwzyDDqSPVyFo4p3wYG18MNcGPWAWqnuDd7W2zqWp94joV5L06S2UHEKqs549/5O9q8CpOoPYjBoRkRMtls8ALwvhIgBdgN3oBzhP4UQdwL7AT8afxtaFNkj1P3+lSodOCEdcu/1/jzxqerem9BWeq+GLX9dFyW26ey9jn3LwR5TN3nAYNCEiHEkUsoNgLv/oomh1mKIANJ7KCew8lUo2gGTn1dpuN5ij1Ln8Sa01fvyhtuTrf7lpYW+OZIDa1Q5lOh4719rMASZiAhtGQxeI4QalRTtgOQsOP9O38/l6aLEsmIVAqs/0Q4uq9t9mHCvroTDG1QtLYNBQ4wjMbRcsnPV/bhHIdqPjnae1ttylkZJ79VwnzO05Uvv9oLNUFNhHIlBWyImtGUweM3gW0DY1L0/JKarsifN4czYcl3V7iQhHRC+ZW4dXKPuOw3z/rUGQwgwIxJDyyUhFUY+4H9xw/SeULyz+erCR7dCdKJqllUfe5RKJT65z/v3P7gWWnVQfUYMBg0xjsRgaI4RP4e4FJj7WO26FHcc2QTt+4PN7n5/90mw6Z+weY53739wrQlrGbTGOBKDoTkSUmHib9TK8i0fuz/GUaPmMjIHNn6eK15SCQCfToddCz1775KjqmKxcSQGjTGOxGDwhCG3KifxzZNQUdpw//HdUFUGmQMaP0d0PNz0oQqVzZ4Kh/7d/PseXKvujSMxaIxxJAaDJ9jscNnvoeQILP19w/1HNqr7pkYkoCoTT/1YFYN8/1pVkbgpDq4BW3Tz5zUYwohxJAaDp3QaBgNvhhWvNnQARzaqlecZvZs/T6tMmPqpevz3KXD6SOPHHlynRjn+pC8bDEHGOBKDwRsm/RdExcG8GXUn3o9shLZ9Pc8QS+8O//GRWp/y/rVw5mTDY2qqVPjLhLUMmmMcicHgDcntYPyvIH8+/PCV2ialaqLV1PyIOzoMhRv/rtaozL65YUHHXQtVb5Ps4YHRbjAECeNIDAZvGXa3CmHNexyqzsKpA6rMuy/zGN0mwJQ3VVHGj6fVtvR1OGDBTFWXq5eb2l0Gg0YYR2IweIs9Gi6dpRYXrviDWj8C0N7HCfH+18Ils2DHl/Dlw2qEs/mG19iPAAAM0klEQVQjOLoFJjxlGlkZtMeUSDEYfKHrhdD3Klj6EvSbokqxtOvn+/mGT4eyQlj6oupnsvUTaD8A+v00UIoNhqBhHInB4CsXPwd538DGD1SoKybBv/NNeErV4lr+sno+9WWwmaCBQX+MIzEYfCWlE4z5JSx6NjDrPISAK15W8ySOajV/YjBEAMaRGAz+MPIBOLQucCEoexRMeSMw5zIYQoRxJAaDP0THwc3/CLcKgyGsmACswWAwGPzCOBKDwWAw+IVxJAaDwWDwC+NIDAaDweAXxpEYDAaDwS+MIzEYDAaDXxhHYjAYDAa/ENK1p8KPACFEEbDPepoOHAujHFd00uKKbrp009MUOmnVSUtj6KRRJy2eEAq9OVLKDHc7fnSOxBUhxDop5fnh1gF6aXFFN1266WkKnbTqpKUxdNKokxZPCLdeE9oyGAwGg18YR2IwGAwGv/ixO5I/hVuACzppcUU3XbrpaQqdtOqkpTF00qiTFk8Iq94f9RyJwWAwGPznxz4iMRgMBoOfGEcSYoQQItwaIgFjJ98xtvMOYy//adGORAhht+7D+kURioeFEB2lhrFEYyff0MVuTg26287Yy3d0sp07WqQjEULcLoRYDzyogZZbgUXAYOC0Tl8EYyff0MluoL/tjL18RzfbNUaLm2wXQvQG3gW+BvoDj0gpdwshbFJKR4i1jAKWAsOklOvq7RPhvBIydvINnexm6dHadsZevqOb7ZqiRYxIhBDJzsdSyh3ArcD/AtuA+63tITG8cwhqvedyYDXQx9r3uBDiJ0KIpHB8YY2dfEMnu1l6tLadsZfv6GY7T4l4RyKEeBxYL4SYJYS43dr8g5TyOPAp0E0IMdY6Nqh/rxBiJvAbIYRrPZrpwDtCiA1ACvAA8DvraiNkGDv5hk52s95Da9sZe/mObrbzCillxN6ACcASoAswHjgCDHDZnwQ8BLzvss0eBB2xwK9QxSA/BS6ut/8+YKj1OAP4DJhs7KSXnXS1W6TYztirZdjOl5teXs17ooH1Uso9UspFwCvAf7vsLwPmAKVCiGeEEP8DdA6CjirgS6AvsAoYL4To4twppXxdSvm99bgIOA6kBkFHYxg7+YYudoPIsJ2xl+/oZDuviXRHkgCkCSHiAKSULwCZQojrrOcSOIuaqLoXKJJS7gq0CKlilnlSyjLgH0BHYJgQIhZqU/aEEKlCiBeBAcDaQOtoAmMn39DCbtZ7RYLtjL18Rxvb+US4h0Se3IBkl8e2evtWAte6PL8RWOny/M/WLSHYWly23wO8Dgx02dYF+Bb4A5AYJDulamSnRrWE205udPRpYl9I7dacHh1sB1wIZGhkr0b16GCvejpuAfrrYruA/V3hFtCM0S9F5Xu/C/zaZbsdiHUx9hKgs/U8G3jN+YMPxIVAi8352LpvBfwRuNn64vzE2p4WJDtdYtngXeBFl+22MNipKS1htVMjev8I7HHaJVx280BP2G3n8tn+GWjlsl2Ew17N6Am7veppHQhsRM3FDKynNSzftYD+feEW4MbgAvXjPB011LwMyEXFO39W79iu1vEzgbeBnwPzgDfDpCXF5fkvgBNAPnBZEO10NyoGfJX1xfsOuDRMdvJUS8js5E5rvefvA/8Gpjn/oUNlNx/1hNR21t9vA24CTgPXNaY/VPbyUk/Yvmv1dP0KuLuJ/UG3XVD/vnALaOJLcBnQw+X5Q6gFOVg/Wo8DRcAYoDUwCngHeDRMWo6gRi0C6A3sBp4IgZ36AlHW47bAP60fceeV2K9DaCdPtITMTs1odWp6ALgLNdo8z2X/DFTr0qDYzUc9BaG0XT1944BXgcHW8+tQ8w4xofie+ahHi++a9fwd4Err8cOo0VRKqL5rwb5FoQlCiPuBiUKIpcC7Usq5Qgi7ECJKSlmNWkC0wzq8LXAK6CmlPGFtWy6EWCWlrAmTlr5OLUKIvag4aJm/WprQtgSYLaXcZm0fjBoGR6G+mGeA/yQ0dvJGS0js1ITWxcA/pZSHhRAxqH/o24BM4EYhxBpUyuhp1MVDwO3mh54+obJd/f8BYBnQC3hdCNEG2IwKEx1FjdiD9j3zQ48O37WPpJSHgMNAWyHEp6gLglzgTiHEs5bWoH3XQkK4PZnlkaegQkfjgb+irjQGWfucV7h/A0a6ea2det4/jFqiwmCngda+rkC29TgJ9eUcHGI7eaolqHbyUOsQa9/T1r0zVLIdl7h5oO0WAD1Bt50bfa8BPYEs4AVqRwFpQDF1Y/6hsJc3esL9XXsNNSq/EVgI/M7FTgux5mqCZbtQ3XRJ/80F3pAqf/q/UJONvwCQUlZbKXudgO+FEB2FEHfBudo4NdL6FDTQUh1AHZ5qe8jStltKud96XIpKeUx10RYKO3mqJdh28kTrfda+y62r3BnAv1BzPGUuWgNtN3/1hMJ29fXtBR6TUh4G/ktKuR5ASlkMfAK0cdEXCnt5oyfc37V9wK+klLNRI5FoIUQ7qUYbK4BuLlqDYbuQEFJH4szddvN8N2pYipRyH/B/QKIQ4iprf2/U1caDwOcEYOGQTlr81JYghLiy3vFPAv2ArdaxPn85ddISYK0pQogRqIVfK6SUg6SUtwDtseow+atVNz1+6PsCSBZCXCmlPOty/FOoz3Z7IPTppidAWj8HMoQQo4HfoRZK/srSei2wONhaQ0GoRyTRrk9cjDcHKHf5sT6CyvjpY31AXVH/TF2Ay6WUs+q9PtK1+KutL4AQ4lIhxDLUsP9aKWVBC9MSSK0LgbGokhMzXF42xXmF2wL1+KPvO2o/2zFCiEWoz/YaKeXRFqonUFoXokLh61Gr1XegFiBOCuJnG1JC4kiEECOEEB+hCqP1FbVNWpyT/SdQ9XDutYZ4p1Cx9XjrA8oHRksp75VSHmkpWgKpzdq/HZgupbw1nHYKtJYgaU1E5eU7hEqksAG4XuG2FD0B0uf62e4Ffi6lvCUQn61ueoKgNdG6IaU8LqV8U0r5KynlwWBqDSWhqL7ZFjWZOBc1EfYg8DNQcw7WYfGomvtHgD8JIbJQTWeqrOM2Syn9Ll+gk5YAa6u0jtsrpdzSkrQEWWu1dVyNDFBpbt30BFif87M9IK0MvZamJ9haWywy+FkMFwEfWo8TgcmoBX29rW3Pogw/GDXf8Cxq2Po6Aa5uqZMWnbXppCXStOqmR3d9uulpKVpDfQuGsa8GnkDNH4Aq0bwT6GY9TwV+C8xCxQk/cO5zOUeg6j1po0VnbTppiTStuunRXZ9uelqK1nDfAml0Z03/JagFQYVYBchQud4vW49twGhUfZxmC/tFuhadtemkJdK06qZHd3266WkpWnW5BdL4w3FZ0o8qjLbCejwQVTdmkvW8DyotLjEYhtdJi87adNISaVp106O7Pt30tBStutz8KpEihLgV2A+sAb5HLaxy9kjehrV2AFW+YDbwshDiamAiqv5NNASmB7FOWnTWppOWSNOqmx7d9emmp6Vo1RGvHYkQQqAWSX0AOIBdqCJzD0opjwoh7FLKGiFEH1QBMqdx/2ZlPTyOWtR3l5TypD/iddKiszadtESaVt306K5PNz0tRav2eDnkc1Yo7Qn83Xocharz/0m9Y94Frrcet3c5R0wghlI6adFZm05aIk2rbnp016ebnpaiNRJuHo1IrMU2MwG7EGIuqklMDZyrP/UL4LAQYpyUcrH1slJgjxBiJvBTIcQlUsqDUkq/8ql10qKzNp20RJpW3fTork83PS1Fa0Thgeceh+rs9QZq2LcEVep6PzDM5bh7gUVOT47qL7wPeJlm2mB6etNJi87adNISaVp106O7Pt30tBStkXbzxPhjgFtcnr9uGfp24Htrmw0Va/wnkIOqaPkyVmnsAH4RtNGiszadtESaVt306K5PNz0tRWuk3TwxfgIQS2288D+A/7YebwAesB6fj2puFMwvgjZadNamk5ZI06qbHt316aanpWiNtFuztbaklOVSygpZ263rIlRLTYA7UFVxvwQ+RKXNNSixHCh00qKzNp20NIduWnXTo7s+3fQ0RSRpjTQ8Tv+18qkl0A61AAegBFVC4Dxgj1QtJZGWWw8WOmnRWZtOWppDN6266dFdn256miKStEYK3lT/daAW3RwDBlie+ynAIaVc5jR8iNBJi87adNLSHLpp1U1PfXTTp5uepogkrZGBN3EwVOkAB7AMuDOcMTmdtOisTSctkaZVNz2669NNT0vRGgk3YRnVI4QQHVF1Z16SUlZ447ACjU5a6qOTNp20NIduWnXTUx/d9OmmpykiSWsk4JUjMRgMBoOhPqHu2W4wGAyGFoZxJAaDwWDwC+NIDAaDweAXxpEYDAaDwS+MIzEYDAaDXxhHYjAYDAa/MI7EYDAYDH5hHInBYDAY/OL/AwepA45iFQWZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "win_size_days = 20\n",
    "predict_future_n_days = 10\n",
    "predict_df = train_and_valid(available_factor_df, y_arr, win_size_days, predict_future_n_days)\n",
    "predict_df = pd.DataFrame(predict_dic).T\n",
    "predict_df.plot()\n",
    "predict_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Accuracy on train</th>\n",
       "      <th>Accuracy on test</th>\n",
       "      <th>Log-loss on test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>83.2674</td>\n",
       "      <td>68.5575</td>\n",
       "      <td>62.7726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-07</th>\n",
       "      <td>81.8760</td>\n",
       "      <td>68.7023</td>\n",
       "      <td>64.8792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>81.7248</td>\n",
       "      <td>69.6921</td>\n",
       "      <td>64.4444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-11</th>\n",
       "      <td>80.0597</td>\n",
       "      <td>70.3892</td>\n",
       "      <td>63.7530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>78.4332</td>\n",
       "      <td>70.1299</td>\n",
       "      <td>63.5334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>67.3746</td>\n",
       "      <td>68.9792</td>\n",
       "      <td>65.9516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>68.1996</td>\n",
       "      <td>70.5419</td>\n",
       "      <td>64.3474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>67.7393</td>\n",
       "      <td>69.3215</td>\n",
       "      <td>66.5333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>68.0083</td>\n",
       "      <td>71.5655</td>\n",
       "      <td>64.5037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>68.1246</td>\n",
       "      <td>72.0133</td>\n",
       "      <td>60.4078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Accuracy on train  Accuracy on test  Log-loss on test\n",
       "2020-02-06            83.2674           68.5575           62.7726\n",
       "2020-02-07            81.8760           68.7023           64.8792\n",
       "2020-02-10            81.7248           69.6921           64.4444\n",
       "2020-02-11            80.0597           70.3892           63.7530\n",
       "2020-02-12            78.4332           70.1299           63.5334\n",
       "...                       ...               ...               ...\n",
       "2020-06-01            67.3746           68.9792           65.9516\n",
       "2020-06-02            68.1996           70.5419           64.3474\n",
       "2020-06-03            67.7393           69.3215           66.5333\n",
       "2020-06-04            68.0083           71.5655           64.5037\n",
       "2020-06-05            68.1246           72.0133           60.4078\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CBRgYGDB79mxGjBjByJEjAejduzfTp0/nqaeeYsyYMeXme5BLxleENG01EkM629CttTmf7w0hI7fqEtESiaTuiCrqB86YMQMvLy+GDx9eo3N0dHQ4ffo0Y8eOZfv27QwdOhSAlStX8sEHH3D9+nW8vLxITEyssQx1LRl/7tw5zp07x8WLF9mzZw+gloyfP38+AQEBdO3atdq5Ggq5ImkkFEXhrREdGf3Ncb4/HMHLg9trWiSJpE7UduXQUJiZmWFubs6RI0d45JFHWLduHf369cPc3BwTExNOnjxJjx49yrTlXb16dYVzubq6EhkZSVhYGC4uLqVzZWRkkJWVxfDhw+nRowcuLi4AhIeH0717d7p3784ff/zB9evXsbS0LJ3vQS4ZXxFSkTQiXRzNGdrZljXHrvJc/7YY6GprWiSJpMmQlZVVahICePnll/n555+ZN28eWVlZtGnTplRR/PTTT8yZM4dmzZrRv3//agspGhgYsHr1asaPH09BQQE+Pj7MmzePpKQknnzyydKmVsuWLQNg0aJFhIaGIoRg0KBBeHp6lplv9OjRnDhxAk9Pz9IGXLa2tjVWJCUl4w0NDTlx4gRbtmzhhRdeIDU1lYKCAhYuXEj79u2ZMmUKqampCCHKlIwfN24cv//+O1999RWPPPJIbX7NdaJJlJGvTxqzjHxFHA6JZ+qq06x42psRHnYak0MiqQ1NrYx8RkZGad7JRx99xM2bN/niiy80LFXT4UEtI//A0NvFCltTA347E61pUSSSB5Zdu3bh5eWFm5sbR44c4e2339a0SA800rTVyGhrKYzq0oofjkQQn55LCxP96k+SSCS1YsKECUyYMEHTYjw0yBWJBhjXtRWFRYLfz8VoWhSJRCK5Z6Qi0QAu1iZ42puxJUCatyRNh4fNn/qwUpe/s1QkGmJsV3uCYtM5EZ5Y/WCJRMMYGBiQmJgolckDjhCCxMREDAwManWe9JFoiCc9W7HyUDhTV51i0ZAOzO7TBi0tRdNiSSQVYm9vT3R0NPHx8ZoWRdLAGBgYlAmzrgky/FeDJGfm8cbWi+y+FEsfFys+e8oTG9PaPQlIJBJJYyDDf+9TzJvp8e0Ubz4c407AtWSGLj/MnkuxmhZLIpFIaoVUJBpGURQm+Tqy84U+tDI3ZO66AN7cdpHsvEJNiyaRSCQ1QiqS+4S2LYzZ+mxv/tW3DRtORTHyqyNcupGqabEkEomkWqQiuY/Q09HijeEdWT+7Oxm5BUz8/qRcmUgkkvseqUjuQ3q7WLFsghfpOQUcDL6laXEkEomkSqQiuU/p7myJlbEeuy7c1LQoEolEUiVNQpEoitJBUZRzd7zSFEVZqCjKEkVRYu7YP7z62ZoG2loKw9zsOBB0i6w82QhLIpHcvzQJRSKECBZCeAkhvICuQBawrfjwspJjQog/NSdl/TPc3Y7s/EIOBskkMIlEcv/SJBTJXQwCwoUQ1zQtSEPj62yBlbE+uy7eqHxQUgT8sRDysxtPMIlEIrmDpqhIJgIb73i/QFGUC4qirFIUxVxTQjUE2loKw91tqzZvXf4dAlbDxS2NK5xEIpEU06QUiaIoesATwObiXd8CbQEv4CbwWSXnzVUUxV9RFP+mVitouLsdOflF7L0cV/GA+BB1e/p7eMjK3UgkkvuDJqVIgGHAGSFEHIAQIk4IUSiEKAJ+AHwrOkkI8b0QopsQoluLFi0aUdx7x8fJgrYtmrF4xyXC4zPKD4gPAi0diL0A0X6NL6BEInnoaWqKZBJ3mLUURbmz6floILDRJWpgtLUUVk33QUdLYdqq09xKz7l9UAhICAWPCaBvpq5KJBKJpJFpMopEURQjYDCw9Y7dSxVFuagoygVgAPCSRoRrYFpbNmPVdB+SMvOYucaP/MIi9UDaDchLh5ZdoMtkuLQd0isxgUkkEkkD0WQUiRAiSwhhKYRIvWPfM0IIdyGEhxDiCSHEA5u952HfnI/HehAYk8afF4s/ZkKwum3RAXxmQ1E+nPlZc0JKJJKHkiajSCQwwt0OF2tjVv4ToXaqiy9WJFYdwLItuDwK/qugMF+zgkokkocKqUiaEFpaCnP7tuHKzTQOhyaoisSgORhbqwN85kD6TQjaqVlBJRLJQ4VUJE2MJ71aYmOqz3f/hENCiGrWUopb9LYbDM1bw+kfNCukRCJ5qJCKpImhr6PNrD7OHA9PpCAuCKza3z6opa36Sq4dg7hLmhNSIpE8VEhF0gSZ5OuIvX4WOjmJ6orkTrpMAR0DuSqRSCSNhlQkTRATA13GOWYBUGh5lyIxsgD38XDhF8hO0YB0EonkYUMqkiZKf8skAC7n25Y/6DsH8rPg3IZGlkoikTyMSEXSROmofZMsoc+f13XKH7TzBIfu4PcDFBU1vnASieShQiqSJop+Shhxeg7sr6xXie9ctcR8+IHGFUwikTx0SEXSVEkIodCiHSFxGUQlZpU/3vEJaGatrkokEomkAZGKpCkiBKTH0qJVGwD2XamgvpaOHnSbASF/Q9LVRhZQIpE8TEhF0hTJTYeifMwsbXGxNmZ/UCWFGrvOUHNL/H9qXPkkEslDhVQkTZGsBHXbzIpHO9pwKiKJUxGJ5ceZ2kHrXnDteOPKJ5FIHiqkImmKZKmhvxhZMrm7I63MDZn4w0k++Tvodon5Epo7QmpM48sokUjuLxqwg6pUJE2RrOLVh5EVDhZG7HrhEcZ3tWfFwXDGfnuciDs7KZo5QEYcFORpRlaJRKJ5ctNh1RAI3t0g00tF0hTJLDZtGVkAYKyvw9Jxnqyc4k1UUhYjvjzKxtNRaql5M3tAQPoNzckrkUg0R1Eh/DZHbcWtXUHeWT0gFUlTpHRFYllm91A3O3a/2Jeurc15Y+tF5q4LIE3PRj2YGt3IQkokkvuCfUsg5C8YtlTtWdQASEXSFMlKAG090Dcpd8jWzIC1M315e0RH/gmOZ8Y2tZtiZnxkIwspkUg0TuBvcPxLtVeR75wGu4xUJE2RrER1NVLSh+QutLQUZj/Shu3ze5NvpNbi+vb3f5j4/QliU3MaU1KJRKJJLv4G5k4w9KMGvYxUJE2RrKRyZq2K6NTSlG0LB5NvYMHgVvmcjUrhv39eaQQBJRLJfUH8FbX2XgP5RkqQiqQpkplQI0UCoK2loGvugKdpJnP7tmHH+RuciUpuYAElEonGyc9Wq1q06Njgl5KKpClSYtqqKab2kBrNvH5taWGizwc7L6sRXRKJ5MElIQQQYO3a4JeSiqQpkpUIzaxqPt5MVSTN9HVY9FgHzkSl8M2hcHacv8GvftdJzcpvOFklkoeJ3AxYNRSiTmpaErgVpG4bYUXSsIYzSf1TmA85KbVbkZjZQ24a5KQytqs9a45H8snfwaWHP90TzIdj3BnU0aYBBJZIGpibF9Snb/dxmpYEIg5C1Am11bVjD83KEn8FtHTAok2DX0oqkqZGdrF/o1aKpJW6TY1B28aMdbN8CY/PxNxIl9TsfN7eHsisn/15qps97z3phoGudv3LLZE0FIc/gSs7QM8YOgzVrCyhe9VtyG7IzwFdg8a79pm1kBIFA99W398KAksXtRJ4A9MkTFuKonRQFOXcHa80RVEWKopioSjKXkVRQou35pqWtQzHv4bdb6jlCeqL0qz22igSB3VbnJRoaayPr7MF7WxM6OZkwY4FfZg/oC2/+kcz5cdTJGbk1p+8EklDkxCqbrfP02zirRAQth+MbSEvA8L3N+71T3wDR5dBdor6Pj4IWjS8fwSaiCIRQgQLIbyEEF5AVyAL2Aa8DuwXQrQD9he/vz9ICIW978DJb+Db3nD1sBpBEXUSUq7Xfd5KstqrxLR4RZJW8ZdMT0eLRUNc+frpLlyMSWX0N8cJu5VR4VhJ3RFCyCCH+qawAJLCoePj6s9bZqnmX00QH6R+x/otAkNzuPx74107K0k1ZRUVQNg+yMuC5Eiwbnj/CDQRRXIXg4BwIcQ14Eng5+L9PwOjNCbV3ex/D3QNYcL/1MTBnx+HL73UwmkbJtR93hJFUhtnu4ktKNrVPq2N9GjJxrk9yMorYMw3xzgellB3OSXlmLN3Di8ceIG8QllAs95IuQaFedB+KDy+HK6fhIA1mpGlxKzVfhi4joDgv6CgkVb3USfUraIFQbtuR2zJFUmlTAQ2Fv9sI4S4CVC8tdaYVHcS7a/abHs9rz4pzTsGIz6HJ1eA2zj1yaWu1Xiz6mDa0tJWVyU1KCfv7WjOtud6Y2NqwNRVp/nV7x5WT3eRnJlHwLXkh/KpPL8on4C4AA5FH+Lfh/5Nvqaemh80SsxaVu1VZ7uNO5zfpBlZwvaBdSfVJ9lplBrgEn6wca597Tho64P7eFWO2AvqfrkiKY+iKHrAE8DmWp43V1EUf0VR/OPj4xtGuBKEgL2LoVkL6Dlf3advDD6zoMsUaPcYiEJIrmP725JeJIYWtTvPrFWN7ccOFkb89lwvera15NXfLvDRX0EUFdX+5h+fnsuuCzd55/dAhiw7TJf39zL22+P87+S1Ws/V1IlKi6KgqIDeLXtzKPoQrx15jYKiAk2L1fRJCFG3li7q1n0cxPg3fnvp3Ax1VeAySH3v3A8MzODy9nqYO11NLqyKa8fBvht0Hq0qML8fQUu3USK2oIkpEmAYcEYIUdJbNk5RFDuA4u2tik4SQnwvhOgmhOjWokWLhpNOCDjyGVw7Cv1eq7CoIi3aq9uSL0BtyUoEfbPaR2KY2UNqzVcXpga6rJ7uw+Tujqz8J5zn1p8hO6+w2vNOX03izW0XGfTZIXz+bx/zN5xhS0A01qb6LBrSgR5tLPjoryCik7NqJ38TJywlDIAXvV/kVZ9X2XttL28efZPCoup/p5IqSAxVH9qKWyrgNlbdBm5pXDkij6gmNpfB6nsdPXAdCVf+uB0gU1c2TIDlHpX7XHIz4OZ5cOwJbfqDrpH63tIFtHXv7do1pKkpkkncNmsB7ACmFf88DWhE79ZdFBbAzpfgwPvg/pTaL70iLNup27oqksyE21+a2mBmD2k3oKio+rHF6Ghr8cEoN94e0ZG/L8fy+tYLVY6PiM9gyk+n+OPcDRwtjHh9mCvbnuvF+cWPsW5Wd+YPcOHT8Z4AvLH14kNl4gpPCUdL0cLZzJlnOj3DQu+F/HX1LxYfX0yRqPnfRHIXCaGqWauE5g7qDfXilobrCFhUqL7uJGw/6DYrmzvS+0V1JfHP0nu4VhHcOKvmjv06FTZNhvTYsmOiT6tWjta9VL9s24Hq/kbIaC+hySgSRVGMgMHA1jt2fwQMVhQltPhYw5a4rIzcDNg0CQJWQ5+XYcz3lRdJ0zdW/RXx97AiqY1/pATTVlCUD5kVLtoqRVEUZve055jVR+Rc2M6uCzcrHCeE4K1tgejraLH/3/1YPcOXef3a0sXRHF3t2/9m9uZGvD68I0dCE1i+L5SwWxkU1sFs1tQISwnD3tgeAx01r2CW+yye83qO38N/5/2T7z9USrVeSQgBq3Zl97mNVf2QcZca5prb/gUbJ5bdF+0H9l1BR//2vhYdoOs08P8JEsPrdq30m5CfBY/9Hzz6rur/+NoXzqy7rSivnVCDaRx81fcdhhdfv3H8I9CEFIkQIksIYSmESL1jX6IQYpAQol3xNqnRBUuPgzUj1D/wyGXw6OJKy7uXYtXuHkxbCbWL2CrhrlySWhG2j5bpF3jG2J+3tl/kVlr5UvRbAqI5EZHI68NcsTatOglrsq8jfdu34Iv9oTz6+T94vbeHA0FxVZ7T1AlPCadN87L26nke85jjPoctIVv48PSHUpnUlsxE9cHK8i5F0nm0emO9WCtXas3Iz1GjoiL+uR1mXJCrKq2WXcqP7/8G6BjAvsV1u15SsQJq0R76LFQDd2zdYMcCWPuk6gu6dhzsPG6b0jsMU2Vp1zBNrCpCZrbfC/HB8L9x6s190iZoP6Rm51l1gHMb1CeK6pTO3WQlga1H7WW1cFa3CaGqU642FH8he+iGkZ1VwMyf/WhnbUJ6TgGmhjrYNzdk7clrdGttziQfx2qn09JSWD3dh9Bb6QTGpLH62FXmrz/Lprk98HRoXttPVinX06/z19W/GNFmBK2MW9XbvADR6dGcunmKUzdPcS7+HHM95jKufcUlOvIL8/cbJngAACAASURBVIlKi2Kg48Ay+xVF4fkuz5NbmMvay2vR09Lj393+jVLb/4mHlcQ7IrbupJmVat4J3AqDFoNWPT4vRx1XVwgAcYHqDfvWZXW1b+dVfryxNfReCAc/UHPIals2JVH1rZUGE1i5wLSdqvVj72L4pqeaO9L9X7fPMbKAuYdq+8nuiSazIrnvuHYcfnoMCrJh+q6aKxFQVyR56eVtndUhRN1NWxZt1fDAW7Vc7udmqPHwBmboZMaybIgVt9Jy8b+WRExKNqcikvj6YBjZeYX8d4w7Wlo1uwlqaym42poyrqs9a2b4YmWix8w1fpy7nkLAtST2XY4jK6/mUU0How4ycttI1l5aS05BDnsi9/DUH0/x1dmvGLF1BG8dfYuIlIjaffY7SMhO4M+IP1l8fDFDfxvKsK3DWHJiCX5xfmgpWiw/s5y0vLQKz72Wdo0CUUDb5m3LHVMUhVe6vcIk10n8fPlnvjr7lVyZ1JSSVf3dpi1Qo7dSo1T/QX0Stl/N1QA1zB/gxjl127ICRQJq9KaJHex5u/Z+m8RwdUVj0vL2Pi0tNQp0/klw7qsqsbYDajdvPSNXJLWlMB/Ob4Rd/4bmrWHKFrUDWW0oeYJKCAZTu5qfl5cJBTl1UyTaOuryOO5y7c4L/lNVlo8ugd2vMdzsGsPfGl9mSH5hEbkFRRjr1+3fqYWJPmtm+DL22+OMWnGsdH9HO1N+mtaNls0NqzxfCMHX574mNjOWT/w/4bsL35GWl4aHlQeLfBbxd+Tf/Bb6G3+E/8Egx0HMdp9NZ6vONZbvM//PWHNpDQAmeib42PgwtdNUutt1p41ZG4KTg3nqj6f46eJPvNT1pXLnh6WqT5UuzV0qnF9RFF73fZ28wjx+uPgDutq6POv5bI3le2hJCFEfjppXsAp2HaHegC9urt/iiWH71Jt33GWICQDmwM1zaqivuXPF5+gZwYC3VHPU5e2q6a2mJIarD4EVrarM7OHpX9T6Wuat6/Rx6gupSGpKYjj4r4ILv0BmPDj2gonr6xZBVapIQtVwveq4dUX9stSlPMqdWHeGq//U7pyLm9V+Jj6z1Ii066fAo6wi0dXWKuNQrwttWxiz9dlenIxIwq65ARk5Bby59SJPrjjGj1O7VWnyOhV7ipDkEN7r9R72Jvb8dPEnOlh0YIHXAnS1dfGy9mKOxxzWX1nPxisb2Re1j14tezHbfTbdbLpVaUraGbGTNZfW8ETbJ5jkOomOFh3R1ipb1NLVwpURbUaw/sp6JrlOwraZbZnjd0ZsVYaWosU7Pd8hvyifb859Q3R6NK4WrrQybkUPux4Y6RrV8Df5EJEQqpp8tCooMqpvoma7X9qutpmtjzDYlOuqE7/LM2qIbUyAuv/GObULYVUmSa+n4eS3sG8JdBhRNnw/O0UNH+74ePnzEsOqjr5SFI0rEZCmrZoTcRBOrQSH7vD0rzDtj7opEVBLluiZ1MzhnpsO3/dXY8kziiOu6uJsB7DppEaBZNUwJiEzEcIPgPtY9YvYqqtagqKBaNPCmKe7OzKggzWPe7bkt+d6oa+jxYTvT/DnxYqjxQDWXlqLhYEFw9sMx8fWh5WDV/JS15fQvePmYWFgwfNdnmfPuD0s9F5IUFIQM/+eydvH3q503rDkMN478R5dbbrybq93cbNyK6dESljQZQFFoohP/D7hQvwFrqZeLTVRhaeE42DigL62foXnlqClaPFer/cY224se6/tZanfUl48+CKPbnmUz/w/IyotqsrzHzoqiti6E/fxqv8yopYPT5URtk/dthsMrbwhIYTouAuIyhztd6KlDY+9p9a/8vux7LGD/we/TClfg6+wQB1vWfFK9n5CrkhqisdE6PgkGNdDQqOiqGammiiSq0dUc1bkEfhrkbrvXlYkoDoHnfpUP/7ydtWR5168AnHsoZbszk2vONmynmlvY8L2+b2Zu9af59afYdGQDjzXv22ZFURESgRHYo7wnNdz1d6oAYz1jJnlPovJHSfz5dkvWXd5HYMcB5VzhN/IuMFLh17CSMeIT/p+go5W1V+VVsatmNJpCqsDV7Pn2h4ABrcezKf9PiUsJYy2ZuX9IxWhraXNkl5LWNxzMam5qYQkh/BL8C+su7yONZfWYG9sT3e77hjrGpNbmEt+UT65hbnkFeapr6I8DHUM6e/QnwEOAzDTN6vRde8kKz+LvyP/Rk9bj6FOQytVnvdMYYHqvK6qXFBlD/lCqDdZtyp6kLQbrCbvBm6pNIJJCEFaXlrNfk9h+9ToR6v20Kob13W0Gb57MksNdRlWkaP9blweVYMADi8Fr0lqYcecNDXwBtTvZXOH2+NTo1T/h0XN/nc0iVQkNUXfWH3VF1bt1YrA1RF+QE108nhKjdSAuisSm07qNq6GiuTiFrXom42b+t7BF0SR6mRsJOeelbE+G+b04NUtF/jk72DC4zP4cIw7+jrqze2rgJ/Q09JjQofaFcI00DHgpa4vceLGCf576r90t+tOM91mCCHYHradj/0+Vn0vg76mhVHNHh5e8n6JQY6DSM1NxT/On9WBq/n2/LdEpUXxqGPtQjEVRaG5QXN87XzxtfMlNjOW/VH7OXnjJHsi91AgCtDT1kNPS0/dauuhr62PnpYeV1Ovsj9qPzqKDqb6purv0dCKz/t/TmvTys0gUWlR/BL8C9vCtpGep7Y++PHij7zS7RV6t+pdK/lrxOXt8Nuse5vD1q3yYzr60Olx1bw1cpmarHcXay+v5cszX7Jv/D7MDaroQlGQp65s3MepD4KtvIkvzhX73bgZwypztN/N4PdhZR+1AsZjH6im8rziStu3LpcN2kksDg6p4Yrkg52XuRCTypoZPhjp3b61CyEIictg7+VY+rW3xt2+9g8X1SEViaawaqc67at7ug/fD86PwPBPVPts1Mm6m7ZM7NSnoLjA6semXFefFge8fdv2a+8DKHD9tPrz8a/U2mH2XesmTw0x0NXmi4letG1hzLJ9wUQkR9Oj8y22hf1GlhJFa91BWBjU3syoq6XL4p6LmfrXVL488yXeNt5suLKBM7fO0M2mGx/0+aBWYcOKouDZQs3cf6TVI8RlxrHy/EqACiO2aoNtM1smd5zM5I6Tqx0rhOBS4iUORB0gJTcFBYW91/Yyd89cfh72cxkfTpEo4mjMUTYGbeRYzDG0FW0Gtx7MRNeJJGQnsCxgGfP2zWNkm5G82f1NTPTqcSVakqQ3828176PiT1P5+dq6YOtZ9TXcxsHZ/0HI39C5bHHwW1m3WHFuBXlFeUSkRtDVoIr/4+jTaqSlS/EDgYEZGc0dgDxOGhqQaGBKjR7vbN3AazKc+g58ZsPp76Glt2q2vnWldFhkQiZX/jnCMODD03m0T4hmVJdWaFcSFXkjJZs1xyMpKBK8svk8K572BuB/J6/xw5GrRCWpIcsGutpSkTxQ3Olwb+Vd8Zikq5AUAd3nqV+aSZvUcgkGdfxHUBTVvHWrBpFbgb+pW/ext/cZmIFNZ7Xmz4VNqmy3LsOEdXWTpwak5aURmBBIYEIgYVoXsXO/QHhBEuHhQF5LmhdOIDLOi6y8gjJPYSUIIap0pntZe/FUh6fYELSBDUEbaGXcije7v8mEDhPQUuruQlQUhcU9FxOaEkpocmilEVsNgaIouFm54WZ1+2l9TPsxzPp7FnP3zuWt7m+RW5hLeEo4m0M2cz39OlaGVjzr+Szj2o8rswLr79CfHy/+yHcXvuPsrbMs7rmYHnY96ifXJfU6GNs0bEta577QzFoNGrlLkSwPWE5OgZpcez39Ol1tqlAkoXvVtrXOfUt3ZVg6Q3YwhYrCnqi9THKdVDOZBr6lfr82TFDN26NWqua3YkVyITqFGav9WJh/hQwtIzZeyiYt4DzfH47gtWEdGNDButzvf/WxqwhgZm9nVh27ykd/BRESl87B4Hh8nSz4V782PNrRBptqkoXrilQkmsK62My062V1uev8SPkx4QfUbdviiqKGze/dpGTTSbXJFhVVnah1cQu06la+eqiDrxq9ZmqvrkqiTtYtsbIKcgtz+cTvE07dPEVkWmTpfidTJwa27oO5dlsy0xx4oU9fguMymPj9SXYHxjLG27507PnrKXy6J5gT4Yk83d2RFwe1w9K4Yh/Ki94vYqhjiI+tD71b9q43n4CRrhFfD/yav67+RTvzKpzCjUBny858PfBr5u2bx+w9s0v3e1t780KXFxjkOKhMcEIJetp6POf1HL1a9uL1I68zd+9cnM2cGdtuLKNcRtXJB1NKWsztpmsNhZa2WjLF/yc1OspQjf47H3+ePyL+YHrn6ay9vJbr6dUUNA3br9bwMjAt3ZVh1hKyg7HS0ufPiD9rrkhMW6q5JUc+Vc3UnUer+V1Xj3A4KJZ5G85h0UyPsS1zMCpoz/m5Q/jzYiyf/B3EzDX+9GhjwRvDOpZGMqbl5LPx9HVGuNvxn5EdSc7K47vDEejpaPHuE52Z2rN1gye5SkWiKSzbwujvYN+78PNIdck8aLFa6qCE8ANg5qiOrS+sO6k22dSoyvNfbgVB3EUY+nH5Yz2eU//5ey5Qn6p2vayWxK/HctVfn/2aX4J/ob99f55o+wRuVm50tuqMqZ5pubHdnfVwtDBis380Y7ztKSwSvLrlAr+dicbcSJchbrasPxXFtjMxLBjowvTeTqX+lRJM9Ez4d7d/15v8d9LSuCWz3O/RD1BPdLPtxrYnthGdEY2xrjEWhhY1Nt15WXux7clt7L66m62hW/nU/1NWnl/JJNdJTHSdSAvDFrW/WaXG3K6G3ZC4j4NT30LQTugyBSEES08vxdrQmmc9n2Xvtb1VK5K0m+r34dElZXZnmLWEWBht15sfYg4QnR6NvYl9hVOUo89CuPAreE9V+7pbd4LCXD7c8CeOFi6snemL0aqFYO+DoiiM8LDjsc42bDwdxRf7QnlyxTFGeNjx6pAO/BUYS0ZuAXP7tkFRFD4c446zVTOGdLalg23DB8WAVCSaxXMidHpStZMe+Ry+e0SNkBrwlppsdPUwuI2p16d9bIojt+IuV65IAreo2bsVJU5ZtYOBxSGzjj3VbdSpelMkZ+LO8POlnxnffjzv9Hyn2vGKojC+qz2f7Q3helIWG05H8duZaP7Vrw0LBrhgYqBL2K10/vtnEB/+FcT/Tl3jtaGujHC3eyhLkTiYOuBg6lD9wAow1DFkdLvRjG43muCkYL6/8D0/XvyRHy7+gImuCfYm9jiYOOBo6oiDiUPpCyAjLwN9Hf3S9wih1n1rO7CKK9YTrbqq/+sXN0OXKVxIuMCFhAv8p8d/MNI1wsHEgej0KmrQlfRedykbMJGhZ4QWWoztvogfth5gd+RuZrvPrmCCCtA3gRfPl1oFRIuOKEA7rvPqtIlYGylqoqHn7eKQutpaTO3pxOgurfjhcAQ/HLnK34GxGOhq06utJW6t1NWhga42Lwxq3BWwVCSaRtdQLTftPQ2OLYeTK9Uok3aPqQ1q6vuLVtIx7dYlcB1e/rgQ6hfOuR+Y2FQ9VwtX1W8SdUINZ6wDQgj84/wx1TPF2siat46+RUvjlrzS7ZUazzG2qz2f7wvh5V/P4ReZzCRfR94YdrvyqYu1Caum+3A0NIEPdl1mwYazrHK8ytsjO+HtWEWkjqRSOlh04LP+nxGRGsGR6CNcT79OdHo0QUlBHIg6QIGouLzNUKehLOy6kFbaRpCfqT4wNTSKojrdj34O6XH8GvwrzXSbMaLNCAAcTBxKQ7YrJGwfGNvejl4sJiM/g2Z6zWhlYk8X6y78HvY7MzrPqLlp9A7T8s4bJowQCjNcsrE3N1KtAogKI7ZMDHR5+bEOTOnRmuX7Q/ktIJoFAzWbayIVyf2CYXN16ez7L/jnYzizVo1kucO5Vy/om6hZ8nGXiUyN5N///JsPen9AR8viG29MgBqf3/fV6ufS0gJ7XzXbvY78efVPXj/yeul7BYXVQ1fXKpO7ZXND+rhYcSQ0gW6tzXn3iYrLn/RpZ8WuFx5hS8B1Pt0TwphvjjN/QFsWDWm8vg0PGm3M2tDGrOxqtKCogNjMWKKOfEzMld9QHHth3G02IckhrLu8jv1R+3m/878YAWrnzsbAfTwc+ZTUCxv5O/JvRrmMopluM0BVJKm5qaTlpZU3nxYWqCZm18fLWQYy8zMx0VVNR5M7TuaVf17hz6t/8njbCjLUqyAhI5d3/oqgm44tngbFibfxxRFcVeSQWJsa8N/R7vzfKDeNr66lIrnfMLWDx5er/d4z4tRw3frGuhP58Vd47chrhCSHcDr29G1FcnGLWr+o48iazeXYAw7sVbPlq8n0T8lJIeBWAH3t+6KrpUtmfiaf+X9GJ8tOzHCbwdWUq9ib2FcdPVMJ8we4UCQEyyZ4oadTeRCBtpbCBB9HRnq05I2tF/n2UDhjve1p06Iec4QecnS0dLAPPYD96bWqiTQlGZyHMtR5KE91eIoXDrzA1yEbGQZomTbCigTUMiM2buwI/oVc7VzGt79d5qfE3HY9/TqdLe96CIkJgJzUChMaM/LUFQmoyacdLTqy4twKhjoNrTBwoSL8I5N4cdM5MvMKMXHxQKskBPj0j2q4flV5MsVoWomALJFy/2LZVu141hBYtGVFYTyXEy+jo+hwLa24h3pRoepAb/9YzUOMS0I3r1dcZVUIwdlbZ3njyBsM2jyIhQcXsvDgQrILsvnuwnfEZ8fzVve3GOo0lGe9nq3101wJPdpYsn52D6xNahbe2Exfh/+M7IS+jjZf7g+t0zUllRC2D/5YqJplu80sLrOudhS0bWbLDLcZROcmctLQoHFMW8UI18f5tTAZL0t3Olh0KN1/pyIpR9g+VRm26V/uUEZ+Bsa66gOIlqLF812eJyYjhq2hW8uNvZvM3AKW7wthwvcn0dZS2Pyvnhg7uKv9RyL+Udt193qhbKOs+xi5InkIOa2vwyoTQ8a2HkZoVsztGk5XD6sdFN3HVz3BnbT0VuPrr5+EDkNLd2fkZbAzYie/hvxKaHIoxrrGjG0/Fmsja7488yUzd88kKDmIUS6j8GhRh/4q9UALE32m9mrN94cjWDDQBRfrxolweaC5eQF+nab64sb/rOYc+f2o5kRZqXb8QY6DMNfSY7OJCb2MrRtNtCN6EKmny39blg21L1EkFTrcw/aqYe4VWAYy8jOwNLidhtinVR+8rb1Lq08fjj5MeOrdnREV8gqKyM0vpEgouHYcxIZxizEz1Cc/qT0/mDaj4+4XGGBkpXZXbCLIFclDxqWESyyM2U3r/AJedRyGk6nT7VyNi1tA31R19NcUPSO1oU+U6ieJSI1gyfElDNw8kP879X/oKDos6bmE/eP382b3N5ntPpul/ZYSlByEobYhL3q/WP8fshb8q29bjHS1WbZPrkrumdRo2PCUupqdvFnNubB1V4/FXSwdpqetx5Palhw0MiQ+p3Gamm4L3cZLEb9gn5/PYMOyfhkjXSMsDSzLr0gy4tUEYJfBFc6ZmZ9ZuiIB1cQ0s9NzxGfH8+XZL8krymO483CeaPsEQxxH4qD7COkJHqQneNBCqxddbbyJKvqDj/3fJTYzllmRm/nW3IyXjQo55vkkFJvNaktuYS5vHnmzUYt8yhVJEyO/ML/G9te7uZRwiTl752CqZ8J3kWEYpd7A0cSRHeE7yM5OwfDKDrWUdQU1iarEsQec/oH0rESm/jWV3IJchjkP46kOT5XJri5hqNNQHIwdKBJFWBnWsdxLPWHRTI/pvZ1YcTCcWX2SZRRXXclJhfVPqY3QZu5Wk+5AjexTtCE2sEw4+bhcWKMF28O2M8djzj1d+udLPxOZFsk7Pd4p5y/IK8zjw9MfsiVkCz2svVnqtwODtPIN5RxMHMrfeCMOqluXQRVeNz0vvdRHUsL+c83IvDofUWCGu6Un89zasuZYJOuPR5KeW8DgTjY8P9AFD/vmCCH44eIPfHX2K/66+hc6Wtq8l5DMejNTXko4yqqEwAq/P9VxKeESf0T8gbOZ8z3/bmuKVCRNiJ8v/cx3F75j5+idta4tdS3tGnP2zMFU35RVg76j5RUvSL5K6zZq292oy7/SITdNTd6qLc594cTXrD3xX1JzU9k0clN5p+Vd1KaxVEMzt29bfj93g3nrAtixoA+2Zg1TRuKBpSAPfp2qNmqbvLmsg1jXQC0HdFd9t9apsXS3MuXXkF+xMrTC3MAcCwOL0ldNo/ZWBa5iWcAyQM3Sv9PHFpsZy0sHXyIwMZDZ7rNZ4Dkf7YBWkHKt3DwOJg6cjr3Lzxe6F4ysKm6hS9moLYDLN9L438lrTPLtja62Fj8evcqq4tIlw9xsWTCgHZ1a3o4KUxSFuR5zaWHYgm1h23ir+1t0OPYtfaza8MyNP3lu33NsGrmJlsYtK7h65YSlqI3UgpODa3XevSAVSRMhLjOOFedWkF2QzW8hv9XqSUMIwbsn3gUFVg1Zpf5jmtlD0lWcvFTFcS3sbzoYWYFTHcKN2/Qn2cCUddH7Gdx6cLVK5H7DzFCXn6b5MOabY8xZ68+v/+qJoV4DlU6/Xygqqrq6QU0RAv54ESIOwZPfVJz3ZOumtqa+89ppN5jSdgwvJp/inePlE08NdQzLKJYyL0N1G5QUxLKAZQxxGsKNjBt85v8Z/R36Y6JnwsmbJ3n1n1fJK8pjef/lDGpdvKows1drfN2Fg4kDOyN2ciTsBpZGxpjoa9EybB+5rQeSkpYLqNG/CgqKAsb6CrmFuaUhxEIIFu8IpLmRHq8OccXMSBf3Vmb4RSYxq48z7Wwq97+VJHkC8PhyWgArU59g0q5JLPpnEWuGrqmVFSI0WTXTBidJRSK5iy/OfEFBUQEdLTqyKXgT092mo6tVs3+ubWHb8Iv1Y0nPJbefbizaQPJVHE3UNqVRKeFq0y7tOvxL6Oiz2rEjWXk3eM59bu3Pvw/oYGvCl5O6MHutP29vD+Szp6qpKtvUCd6lNlOa+nvNunRWxslv4fwG6PcadKmkMrGNm5rkWhIinhkPRfn0t/Hh5LAvSMpJIjknmaScJBKzE0nKSSrzisuK40riFZJyksolOva378+Hj3xISFIIk3ZNYsW5FbQwbMGXZ7/E2dSZZQOWle1M2dyxfAMpwMqgJQLBtLV/U5RnjYcSzg79JN64YM3v5w6U/0jNC8BO7W+Tk1/IT0ev4heZzEdj3DEzUr+XY7vaM7Zr3aLSnMycWNJrCa/88wrLzyxnkY/ai6hIFBGTHkNIcgghKSGEJocSlxXHR30+Kq1YULIiuZZ2jaz8rEbprikVSROgpMDcbPfZdLHuwvz989l/bT9DnYdWe25CdgKf+n9KV5uut596QO0vfXk7RrpGWBtaEZkeCa2q6fJWCfFZ8WwsTGBEZhYuKTfAsmkm+A3qaMOs4uqpi4Z0eLBNXDFn1O3fb8O//qm4XW115GWphQfbDID+b1Q+rtThHqiaQdOKo6PM7DHUMaSVcasa1fwqaUJVomByCnLwtfVFV0uXzladGd9+POuvrAdgiNMQ3uv1XvmbqJmDGll2F6dDVN/K3EGmeFl6Y3/hOCJEYcDwCfTSt0AItaC9EFBYVMTPfmfIAtYejeXTzftJzsqnt4slT3WrW/mZihjiNAT/WH/WXl5LfFY8MRkxhKaEkl2QDairI3sTe66nX2d/1H6mu01HCEFYShhWhlYkZCcQlhLWKFGRUpHc59xZYG6O+xwMdAxwMHFg/ZX11SqSK4lX+O+p/5JbkMvinovLlkW3cIbsZMhOwVHPnCjd6OrbhVbC7sjd5BTl86+MPLi8o3HqJzUQz/RszY9Hr/Kr//U61SsqLBLsDoxlzfGrPOnViik9NN9Pu0JuXQEtXTWa6sIvak/x2nJuPWQlQt9FVdeDK1EkscWKJLVYkdSy8q+iKJjpm2Gmb1Z2lVHMC94vEJ4azkCHgTzT6ZmKE/WaO6jtd/Oy1IhDIDY1hz8CctFtA/YtshnayQ5O+UHLLozqU7F/pItLFpP+hPg0BV9nC6b2dKJXW8t6Tw5c5LOI4ORgTtw8QTvzdoxpN4Z2zdvR3rw9bZu3xUjXiBFbR3Dm1hmmM53EnERSclN4ptMzrLu8jqCkIKlI7kRRlObAj4Ab6sPBTGAIMAeILx72phDiT81I2DBEpUdxIeECr/m8Vvp09bTr03zs9zGXEi5V6LSOSI1gxdkV7Lm2B1M9U5b0WlL+i2de/D75Kq2LtDioq6vmhNSB0ORQLAwscGrTDa78ASM+q9sT7p0UFd77HHWgtWUzertY8ovfdRYMcEGrkkZCJQghOBQST9DNdG6kZHMsPIGI+Ez0tLW4dCONxzrZYN1APSDuiVuX1OoFyddg//vQaVTpjbVGFBbA8S/VEjnVJc4aW6s9QUoc7qkx6raekxHN9M1YM3RN1YOaFyv21OjSysPL94VQWGCEqY4RgYmBiMxElGg/eKTyem/ZhZkAfPt0b7rbdasP8StET1uPtcPWVjmmi3UX/on+ByFEqX+kn30/toVuIyS5Bu2864GmlEfyBbBbCOEKeAIl7cSWCSG8il8PlBIBOH5DdVL2tb/tBC+pE/TO8XdIyE4o3R+TEcPbR99m9O+jORpzlLkec/lr7F8VZ4tbFCuSpKu0zs4gSVubNJ26PVeEpYTRrnk76PSE+rR3p2O1LqTdhI+dYNPk20+vjchEH0diUrI5EpZQ5biTEYmMWnGMGav9+Hh3EDvO38DCSI8VT3uze+Ej5BcW8emexnN41pjcdLWyrE1nGPJ/kH4DTq6o3RyXtqlz9HmpZtWpbd0gtjiXJC0GdAwbpvxPdZgVm55S1FDfsFvp/Op/nSk9nBjuPIxdEbt4a//z5CDUnu+VkJmvKpI780g0hbeNNym5KVxNu1rqH2lnrq5aGsvh3iRWJIqimAJ9gekAQog8IO9+qDHT0By/cZxWxq1ul99GdfB93u9zFh5ayDN/PsOHj3zIn1f/+OmP7AAAIABJREFUZHPIZrTQYkrHKcxyn1V1iPCdK5KUGDBWe3bXNm69SBQRlhLGmHZj1MQtHUM48D6M+b7uEUFBO9XKx6F71WiggW+D79xGW6E81tkGcyNdNp2Ool/78v3aQ+PS+Xh3EPuu3MLOzICl4zwY7m6HsX7Zr9P0Xk78ePQq03o50bll/bc3rTPxxTcX687qasJ1JBxdrlagrkmmuRBqpeoWrtC+ej8doDrcT62Em+fVqCkz+/ptj1BTmhd/j1JVRfL53hCM9HR4fmA7mhu9g00zG7499w1h9g6sse5EZWu0jHy1z3pJ1JYm6WKtmqTPxp0lLCWsNLrN1cKVbWHbKBJF99TtsyY0lRVJG1Tz1WpFUc4qivKjoiglf8EFiqJcUBRllaIoD1Q2WX5RPqdvnqZXy17lbK+9WvXix8d+JD0/nWf+eobNwZsZ7TKaXWN2schnUfV5JvrGqrkhOoDWKTcAynQjrCk3Mm6QXZCttpLVN4aRn0PcJfimp9rTvbDicuL8/RbsW1LxsaBdYNkOFpxWkx13vw4/DIQb52otX13Q19FmrLc9ey/HEZOSXbo/Li2HN7ZeYMjyw5yKSOK1oa4cfKU/T3VzKKdEABYMbIe5kR7v77yMEFX0Hm9s4i6p25KWAo++CwU5cOjDmp0f7aeaqXouqLrL5p14TQZDC/XvGH6o8ar+3o2JnVrSJ+U6gTGp/Hkxlpl9nLFopoeWosWzDkP5+FYCV3TgQMyhSqfJyFMVibGe5lckTqZOmOubc+bWGcKSi60DqKX+swuyq+61Uk80FUWiA3gD3wohugCZwOvAt0BbwAu4CXxW0cmKosxVFMVfURT/+Pj4iobUij/C/2BT0CZSc1Pvea6quBB/gayCLHq1rNgG7dHCg7XD1jLLbRY7Ru3gnZ7vYNvMtuYXsHCGsL045BegoNSppELJUrq0J7nX0zD/lNrPZM/b8MMAtczEnVz5A058rZZkuZvsZIg8Aq4j1BXN5C0wbhWk3VDn2v2mmj3dwEzq7oiWojDos0P8Z3sgn/4dTP9PDrElIJrpvZz559UBPNu/LQa6la+SzAx1eXFQO05GJHH6auOUAqkRt66ArtFtf4GVC3SbBQE/F/fBqIZL29QK0Z2erPk1rV3huRPQeQzkpt57/kpd0dJWnfwpUXy+NwQzQ11m9bnDf3huPUMys7E2sGT/tf2VTlOyIrkfTFuKotDFugsBcQGEpYThYv7/7Z13eFzVtbffPaNebFnFtmRbcq+4g+VuXMC0AA6da1owYAiEkgsmBJKLKRffBC4k1IR8CRDACaYEuMaAC+6VuBdkuTdZstxUbLXZ3x/7jDWSRtL02SP2+zzzzMw5Z878tGY06+y1115L/S86C1PuOO7BZ+onkeJIDgIHpZTOxhdzgCFSyqNSyhoppQP4MzDM3YullH+SUp4vpTw/I6NhqMIbahw1PLPqGZ5b/RwTP5rIjCUzWH1kNQ7p8Ou87lhxeAU2YWNYpts/C1D9IB4a+pBvXe/adIGaSmIQZCW292lE0sCRgApb3PQhXP+uKoX/5wm1DqDsmKoMCyrEUXWm7gl3fguOahVuAasp0TVw/1oYeruK5b+WCytfU22KP57WaOVhn9izFLZ9TreMJOY+OJorB2bxj7UHeHVRPpP6tmPBIxfym5/0JTUxxqPTXX9+J1rHR/PuqoarqcNG4TYVlnIdTYyboWo7zf9t0691OFTjte6T6vQv94iEVLjmz3Dnt3DhE97rDhQp2ZQW7mHhjkLuHtuV1vHWeixHDWz4AFv3iUzIuYhlh5adS7WtT1lVGVG2KGLtelTnHdJuCIdKD1FeXX7uf7F7Snfswh6SFe4RMUcipSwQQhwQQvSSUv4ATAS2CSEypZRWJximAFsaP0tgOFh6kDPVZ7ij3x2UV5czd/dc5u6ZS4ekDlzd/Wqu7n61d6OCJlh1eBX90/u77VUeEJwT7uk9yGndhb2n9np9ip0ndpKZmNlwiC+EumLtMg4WPK0cwPbP1YKwitMqZXTJ7+D47tr2v6BGK0ntVXtUV+JT4Ir/hQE3wpcPwddPqBCFo0ZN2nZq3Nl6jHOVdnkx9L6c7m2T+Z9rB/Kfk3tRXlFD53Tv4+HxMXZuuKAT/2/ZHgpOndVjbUrh9oaFORPTYMwvlSPZvRi6jnP/2oNr1eR8v6d9f/9AfFb+kJJN5f55pCXGcPvIzrXbdy1SiQCTn+ei1Cxm/zCb5YeWMymnYS+SksoSkqKTtOgFArXzJFB7URdrj6Vzq87kHQ9+5lakjEgAHgDeF0JsQoWyngf+Rwix2do2Hng42CKc6XWTO0/myeFPsvD6hbww5gU6JnXktQ2vcfGci7l/wf1+h71OVZxiS/GWRsNaAcE54Z41mKHthrL9+HbWHPHu6j7/ZH7d0Uh9nA7gjnkqnLJvuepJ3+dKtf+YS9XdqjOQv0C1AG4s9p6dC/cshQc3wa8LVCvS0kKvNDfKgTWqH8TZk1BQu2itbXKcT07EydTcHGqk5IPVGoxKyo6pVgHt+jbclzsdWmfDN79WIw93OMNank6ya8gR0kmpOc69YzqR6Dq3tf49SEiDXpcxpN0QUmJTmL9/vttzlFWVaTHR7qRPah/i7OoixfX/sWdqz5CkAEeMI5FSbrDCUwOklFdLKU9IKW+RUva3tl3pMjoJGnkn8rAJG11TVHvRuKg4Lu96OW9PfpuvfvoVdw24i6WHlvLWprd8fo8aRw1f7v4Sh3QwImtEoKQ3JNVqkZo1hFv73krHpI48s+oZKmoqPHp5laOKPaf2nIvJNknOCJi+FG77UjXscb53cX7tMbsXqz7evS9v+lz2KGiTA/ZoSMxQP46BYMP7EBVXqyVAZKclMKFXWz5Ys5+K6pqAndcnCrep+7Z9Gu6LjoNJv1Vpupv+0XC/wwHbPlNpsd6GtTTi60Mx2ITkpt4u81tSqgzB3pdDVAxRtijGdxrP4gOLqaqpanCO0spSkmP06V8TbY+mf0Z/shKz6kQHshKzKCwvDEro3ZWIcSS6kHcij+zkbOKjGpZa75jckQcGP8BV3a5i9o7ZXmVLVDmqWHF4BTNXzmTiRxN5YY0a5fhSRtpjsgarWPWA64mLiuOp4U+x9/Re/rL5Lx69/MDpA1Q5qs5liTRLVCx0GaNGG7FJkJwFxS6Nf36Yq/qheFM4MjFd1W7yl6oz6mq779WQ0Uf9qASQW0d25lhpJW8t3s3eY2XUOMKUxeVs5drWzYgE1GR41hCVwl1ZXnffwTVQckTZKELJLyzl60Nqfiux/FDtjpICNRJtX7sKfFLOJEqrSlldsLr+aSitKtVqRALwxLAneGHsC3W2pcenUy2rOV1xOqjvHRFzJDqx88TOOm063XHfoPv4as9X/HH9H5k1dlajx1XUVLDi0Arm75/Pdwe+43TlaeKj4hnTYQyTciad620eNOxRcOGMc09HdhjJZV0u4+3Nb3Npl0vdlqFwZedJFZZqMrTVFGnd6o5I9q2AnFEQ5dlENqDWPexd6tv7u7Lj/9TczaCbVTju+3eg6qy6Sg8AY7qn079Da176No+Xvs0jNspGt4wkerZLoke7ZHq0TaJnu2Q6pSZgb2Y1vV8c3armlJLaud9vs8HFz8LfLoNVr8NYl9XdWz9TYa1ekRvW+tOSXRTZrbUyrsUbnSO1jNo6ccMzh5MYncj8ffMZ3WF0nfOUVZXRNiF03R09wV1kIC1edXA8duYYKXEpQXtv40i8oLyqnAMlB5rtK94+sT1T+07l7c1vc1u/2+ib1vDqr6i8iOu+uI7is8UkxyQzvtN4JmZPZGTWSOKiwjch++gFj7L00FKeWfUMf7n4L01OJu46uQubsDXrcBolrbsKlQCUFUPxTu9rPiVmqJThmioV6vKVjR+qVc+dx0BVuVo8d3CNqg0VAGw2wUfTR7DtyGnyj5aSd7SEnYWlrNlznM82HD53nNPBjOyWxj3jupGRHOCsoMLtajTS1CRx51GqIvDG2XUdyYFVao4qVp+QjjcUnDrLp+sPMfX8gbDJdm51O+AyUqsN+cXYYxjbcSwL9y/kqeFPYXdZEFtSWXIuvK0zzsZxxWeL6Y6PF3weYByJF+SfzEci6dGm+VDOz877GXPy5jBjyQyeHP4kuZm5dfbPyZtD8dliXhn/CmM6jPG562GgSY9P5+GhDzNz5Uw+3/U5V3VvfK1A/sl8spOzfXd86T2UEyg/rn60QS1A9IZEq8NieTEk+5gtV3IUdi2E0Y+oK/KcUaqr3+7FAXMkAHHRdoZkt2nQhbHkbBX5haXsPFrKzsISdhSU8NcVe3l/9X5uH9WZ+8d3rzsp7CuVZWpl+QV3Nn9szmhY9BycPa3mQ6or1Wgmd7r/OsJAcWkF977/PVLCz8b1goNd6zbbKtquLkoS63bsnJQ9ia/2fMW/C//NBe0vOLe9fptdXXH2lHctpRQMzByJFzgztnq26dnssckxyfx+3O+pclQx7ZtpPPLdI5yuVHHKKkcVc/LmMKrDKCZkT9DGiTi5psc1DMoYxO/X/Z4TZ0/U2Zd/Ip+nVz7Nb1f8lrUFa30Pa4EakYAKbx1YrdJ5va1AnGiFF/zJ3Dr8b5AO6DlZPY9rBR2GwJ7ATbg3RXJcNIOz23D9BZ349eV9ee/OXOY/Mo6L+7XjzcW7uOaNFewvLm/+RM2xezHUVDRM/XVH1mBA1mavFW2HmkrIcl8NV2d2F5Uy5fUVbDt8mldvHkKn1ATVe+fAajXJDtZIrWECwugOo4m1xzJ/X93srdKq0shwJFZoq/hMcVDfxzgSL8g7kUdCVIJHvRMAcjNz+eyqz7h/0P0s2r+I51Y9B8DiA4spPFPIDT1vCKZcn7EJG78Z8RtKK0t56fuX6ux7Yc0L/Cv/Xyw7tIxYe2xt5zlfcHUk+1dD5kDv+8UnWgtM/Zlwd87TpLk4xa4XwqHvVS/yMNAlPZFXbhzMO3cM48ips/zk1WUszvMzqSDvK5XMkDOq+WOdDsNZlcBZnqaRtrO6smbPcX76xgrKKqqZffdwLjnPGrV2ylWj2OJ8lY1WuMNtAkJCdAKjskYxf//8c5lPFTUVVDmqtCiP0hytYloRbYvm2FkzItGGvBN5dG/T3asCaHFRcdwz8B7uHng3c/fMZd7eecz+YTaZiZl1KvrqRo82Pbit3218lv8ZawvWArC+cD2rC1bz4JAHWXDdAuZfN58rul7h+5ukZKtRSOE2NSro5GVYC1wciR//KMX5qg5Ugkt9si7j1Chl30rfzxsAxvbM4PP7R5HZOo7b/7qG3329g+oaH1I5HQ7I+0b1ivEkmSExXa0pcTqSIxshtnVt2nYE8MXGw0x9ezWpiTF8et8oBruGFLOttPr9q6wKC2V1JtpdmZQzicLyQrYeUzXKnHW2dMvacocQgrT4NDMi0QUpJTtP7vQorOWOaf2ncV7aecxcMZPVR1Zzbc9r60ze6cg9A++hQ1IHZq6cSWVNJW9tfIvUuFSu63ldYN7AHq0WRW79TBUN9GXFc5LTkfgR2ireVXc0ApBhZea56e8danLSEvnkvpFcP7QTry3axQ1/WsWOAi/TOY9sgNIC7xYSZg1ycSQbIHNAeCr2eomUkje+28UDH65nUKcUPrl3JNlp9er4pvdQFw8HVjWbEj2241iiRBTf7v8W0KuEvCekxRlHog2F5YWcqjjlsyOJtkXz3JjnqHRUEmWLUmXXNSc+Kp4nhz/J3tN7mbFkBssPL+e2frcFtgd0WvfaH+tOuU0f647YVmCP8TO05caRxKcCInCLHf0kISaKWdcO4A83DSavoIRLXl7Kz/62lrV7PSwGmTcPEJ7NjzjJGqxK2JQdU90NM/XsY19V4+BEWSUA1TUOnvh0C7Pm7eDKgVm8N20YKQluRmBCqO/b/tUuizTdj0hax7YmNzOXBfsWIKWkpKoEiBxHkh6fTvHZ4DoSk7XlIc4yAx4vvnND19ZdeenClyg+U3wuLU93RncYzSWdL2He3nm0jm3NDb0CPK+T1k3dp2RDq0zvXy+Ef6vbK8tU7ai0eiEbe5RabxGIxY4B5MqBWYzrkcG7K/fy1xV7ue7NlZyf04Z7L+zG+F5tG+/omDdPjfgS0zx/M2fiw8bZapLex1bMwaTGIbnr3XV890MRma3jSI6LIu9oKT8f341fXtSr6Q6X2blq3mjvMlUROK7xnjETcyYyc+VMdp7cSVmlNSKJgDkSUBPuW4u3BvU9zIjEQ1w7j/nD2I5jmdJjSiAkhYzHLniMzMRM7ht4X+Djws6RgC+jESeJGb5nbR3fXVdHnfOmq46P/lBRqvrYB5DWCdE8MLEHy2dM4Okr+3Hk1FnufGcdl76ylE/XH6Sq/hzK6cNqjsPb+ljOCffv/6ru6020V9c4eOqzLfzkj8vYcig8SQl/XLiT734o4qZh2VzQOZXYKDuzrunPo5N7N9sm+dyc3K6F7kvGuDC+03gEgvn75mtVQt4T0uLSOHH2BDWO4JXnMSMSD7m1761MyJ5A61iNOt2FiIyEDOZdMy84XdbSLcfsryPxdeTgLmOrznn9DAms/bNq4PXgJlUfLIDEx9i5bWRnbs7N5stNh3nju108/I+NvLown4/vHVkb0tn5jbr31pHEt1FzWMX5EJNcZ6K9vLKa+z9Yz8IdhbSKi2LK68t5dHIvpo3u2vwPuMWp8irW7D1OlE0QG2UjNtpGbJSdOOs+Nsq6j7YRY7c1OO/ivCJeWbCTnw7pwPNTzvO+Em/WYBUWralsdKLdSXp8OoPbDmb+/vl0TFa95iPGkcSnUSNrOFlx8lw6cKAxjsRD7DY7Oa0C+0MQSQStVWenXFUNuL8fE/iJGbVxbm9xOhJ32UgJabVtaX3FWbOr5EjAHYmTaLuNKYM7ctXADny9tYBfzF7PjI838ebUoQghKNr0DXZ7Gg9+cZrY6HXcOiKHsW5aCLslazCc2AOZAyivdrAk7yg7Ckr4eutRfig4zXNTzuOy8zJ5/JNNPD93B4vzinjxukFNlsvffPAUf1uxly83Haai2vMMtBi7rY7DKS6roFe7ZJ67ur9v5dyj49Tfd2B147XHXLgo5yJmrZ3FtmL1XUuM0T9rC+qubjeOxNAysUfDuMf8O0eSNSKR0vusouJdqnikux+FxHRV9t5Xqs6q9FJQDb6CjM0muLR/Jo+dOMNzc7fz99X7SY6xMWbvUlbbh1BSUcP2glLW7TvOd/95oftJ6PpkDYatn0DmIO5573uW7jyGEGqdy5tTh3JxP7Uu482pQ/nH2gM8/cU2LnllCS/8dEDtmg2LkrNVzJq3g7+v2k9ijJ1rhnbkqoFZxETZqKh2qFtVTe3j6hoqqmofn62ytlU7qKhyYLfB/eN7EB/jR/ajc2FiM6EtgInZE5m1dhZzd88FImhE4rK63ddkoeYwjsQQ+SRmqPBExekmJ0zdUryrdsLf3XnLj6vmWb6kah9cq9KaIXA9UzzgztFdWJZ/jJlfbKWrYx9Xx57mwkuu4dJho9hRcJrLXlnKi9/k8czVHlSWthqM7Y7tydKdx3hwYg+mj+vW4MdbCMGNw7K5oEsqD83ewPS/f89Nwzrx1BV9sQnBZ+sP8fL8nRwtOcsdozrzyEU9SY7ToKLDoJtV5d92zdsiMymTfmn92Fq8lRhbDDF2L4qLhpFzI5IgpgAbR2KIfJyLEkuLfHAk+Y33Hk9IB6RyJkkehoJc2bMYnCHBkgLvX+8jNpvgxesHcvVry7kr6QAUQXzP8QD0bt+KW4bn8N6qfdw0LJu+Wc30FckZCde/x7Mr00lNLOOecV2bHAF0y0ji43tH8r/z83hz8S5W7CqmrKKGY6UV9O/QmjemDqm7MDDctO2j2v96yKScSWwt3hoxGVsQmjIpJmvLEPn4Wial/DicOd7EiMSKJ/uaubV7sertkdg2JKEtV9KTYlny6HiubbNbzf+kdDq37+GLetI6Pprffr6Fk+WVTZ9ICDYmj2XhzhNMG9OFhJjmrz1jomzMuKQ3H0wbTny0nX5ZrfhgWi6f3z9KLyfiA5OyVdvdSAlrgdIaa48N6loSMyIxRD6+OpKmUn/rnNcHR1JRomp1jX4I8ueHNLTlxCZr1BzPedfU2Z6SEMPjl/ZmxsebGfrsfEZ0TWNyv3Zc3K897Vo1nCR/dVE+reOjuWW4d8kCI7qlMe8hfcsA+ULn1p3pntL9XFvbSEAIQVpcWlArABtHYoh8En0sk9JU6i9YoS18Sy3etwJkjSpDX7BZlScJNYfXq3kjN6Xwb7ggmz6ZrfhqSwFfbyngqX9t5al/bWVwdgqT+7Vncr/2nCiv5G/L9/LttqM8NKmHHnMaGvD86OepcjRsv6sz6fHpZo7EYGgSZw8Jb0cOxfmq70hKI1farr1OvGX3YtVNsFMubP5IOZNQ4yyD30hPlQEdUxjQMYXHJvdSLWi3FvD11qO88NUOXvhqBwDJsVFMG92F6eMaCf/9COmT1nyGl26kxqdyqPRQ8wf6iHEkhsjHHu1bOZPifFWapbFquP7U29qzRJXgiI5XbW1LC1UFXlsQpiWrK1QtrINr4dA6NbGf0VuV/mh3XoNmTfURQqh2v+2SuX9CDw6eKGf+tqNER9m4alAHkgLRVMsQVtLj09lUtClo5zffEEPLwNsyKeXHIX9B00UMfa23VV0BhVthzC/V86T2Ksx15nizP+oecfqwCp0dXKccx5GNKv0ZIDkTWmWp+liVJarro5d0bJPA7aN8bJ9s0BJnmZRqRzVRtsD/7BtHYmgZJLb1buSw/BU1Ie78sW/0vD7U2yrepXqZpFul6JOsLo4lBf47ktJCeGWgchxR8WrBYO490OF86HgBtLaarjkcqhhlUjv/3s/QIkiLT0MiOVlxMigFY40jMbQMEtNVT3FPKDkKq99SZVnaNVMaIyHd+3pbx1SlaDKsVcTOH/PSo4AHiwCbYu9S5USufw96XarCeu6w2aB1R//ey9BicDqPY2eOBcWRmHUkhpZBYobK2tqzFD6eBtu/aPzYpS+qH+MLH/fgvOneh7acjsSZDZYUgL7yTvYuh5gk6HVZ407EYKiHs0xKsDK3IsaRCCFShBBzhBA7hBDbhRAjhBCpQohvhRA7rfvIXu1k8J2ktqq/+jtXqCyp1W+5P+7kflUWffDUxhciuuJLaKvoB9Wm1lm/69yIJAApwPtWqEwwuwkmGDzHdUQSDCLGkQCvAPOklL2BgcB24HFggZSyB7DAem74MdJtguqz/pM/wNA71ER0tZtV24tnqXtPC0UmpNfW2/KUY3m15fEBYpPUKMLfEUlZMRRth86j/DuP4UeHs0xKsBxJRFzWCCFaAWOB2wGklJVApRDiKuBC67B3gO+AGaFXaAg7Hc+H26wGUvEpatRRsEltd3IsHzZ8CMPu9nz+IDEDr+ptORxwbCd0Hl13e1IAyqQ4KxHnGEdi8I6EqAQW37CYlNiUoJw/UkYkXYEi4K9CiPVCiLeFEIlAOynlEQDrvm04RRo0wdn5bv/Kutu/ex6iYmGMFymx3tbbOn0Qqs9Aer1y3Unt1CS/P+xbYWVqDfHvPIYfHUIIUuNSg9ZXKFIcSRQwBHhDSjkYKMOLMJYQ4m4hxDohxLqiIr16cBuCQHI71dnP2QsE1IK9LR9D7vTayW9PSPBy1XyRNdHuzpH4PSJZBp0uaHwBpcEQJiLFkRwEDkopV1vP56Acy1EhRCaAde82CC2l/JOU8nwp5fkZGT6UAzdEHtkjlCORUj1f9BzEtoZRv/DuPN4WhDxmdVTM6FV3u3N1u6+cOaGcYc7o5o81GEJMRDgSKWUBcEAI4fzvnAhsAz4HbrO23Qb8KwzyDDqSPVyFo4p3wYG18MNcGPWAWqnuDd7W2zqWp94joV5L06S2UHEKqs549/5O9q8CpOoPYjBoRkRMtls8ALwvhIgBdgN3oBzhP4UQdwL7AT8afxtaFNkj1P3+lSodOCEdcu/1/jzxqerem9BWeq+GLX9dFyW26ey9jn3LwR5TN3nAYNCEiHEkUsoNgLv/oomh1mKIANJ7KCew8lUo2gGTn1dpuN5ij1Ln8Sa01fvyhtuTrf7lpYW+OZIDa1Q5lOh4719rMASZiAhtGQxeI4QalRTtgOQsOP9O38/l6aLEsmIVAqs/0Q4uq9t9mHCvroTDG1QtLYNBQ4wjMbRcsnPV/bhHIdqPjnae1ttylkZJ79VwnzO05Uvv9oLNUFNhHIlBWyImtGUweM3gW0DY1L0/JKarsifN4czYcl3V7iQhHRC+ZW4dXKPuOw3z/rUGQwgwIxJDyyUhFUY+4H9xw/SeULyz+erCR7dCdKJqllUfe5RKJT65z/v3P7gWWnVQfUYMBg0xjsRgaI4RP4e4FJj7WO26FHcc2QTt+4PN7n5/90mw6Z+weY53739wrQlrGbTGOBKDoTkSUmHib9TK8i0fuz/GUaPmMjIHNn6eK15SCQCfToddCz1775KjqmKxcSQGjTGOxGDwhCG3KifxzZNQUdpw//HdUFUGmQMaP0d0PNz0oQqVzZ4Kh/7d/PseXKvujSMxaIxxJAaDJ9jscNnvoeQILP19w/1HNqr7pkYkoCoTT/1YFYN8/1pVkbgpDq4BW3Tz5zUYwohxJAaDp3QaBgNvhhWvNnQARzaqlecZvZs/T6tMmPqpevz3KXD6SOPHHlynRjn+pC8bDEHGOBKDwRsm/RdExcG8GXUn3o9shLZ9Pc8QS+8O//GRWp/y/rVw5mTDY2qqVPjLhLUMmmMcicHgDcntYPyvIH8+/PCV2ialaqLV1PyIOzoMhRv/rtaozL65YUHHXQtVb5Ps4YHRbjAECeNIDAZvGXa3CmHNexyqzsKpA6rMuy/zGN0mwJQ3VVHGj6fVtvR1OGDBTFWXq5eb2l0Gg0YYR2IweIs9Gi6dpRYXrviDWj8C0N7HCfH+18Ils2DHl/Dlw2qEs/mG19iPAAAM0klEQVQjOLoFJjxlGlkZtMeUSDEYfKHrhdD3Klj6EvSbokqxtOvn+/mGT4eyQlj6oupnsvUTaD8A+v00UIoNhqBhHInB4CsXPwd538DGD1SoKybBv/NNeErV4lr+sno+9WWwmaCBQX+MIzEYfCWlE4z5JSx6NjDrPISAK15W8ySOajV/YjBEAMaRGAz+MPIBOLQucCEoexRMeSMw5zIYQoRxJAaDP0THwc3/CLcKgyGsmACswWAwGPzCOBKDwWAw+IVxJAaDwWDwC+NIDAaDweAXxpEYDAaDwS+MIzEYDAaDXxhHYjAYDAa/ENK1p8KPACFEEbDPepoOHAujHFd00uKKbrp009MUOmnVSUtj6KRRJy2eEAq9OVLKDHc7fnSOxBUhxDop5fnh1gF6aXFFN1266WkKnbTqpKUxdNKokxZPCLdeE9oyGAwGg18YR2IwGAwGv/ixO5I/hVuACzppcUU3XbrpaQqdtOqkpTF00qiTFk8Iq94f9RyJwWAwGPznxz4iMRgMBoOfGEcSYoQQItwaIgFjJ98xtvMOYy//adGORAhht+7D+kURioeFEB2lhrFEYyff0MVuTg26287Yy3d0sp07WqQjEULcLoRYDzyogZZbgUXAYOC0Tl8EYyff0MluoL/tjL18RzfbNUaLm2wXQvQG3gW+BvoDj0gpdwshbFJKR4i1jAKWAsOklOvq7RPhvBIydvINnexm6dHadsZevqOb7ZqiRYxIhBDJzsdSyh3ArcD/AtuA+63tITG8cwhqvedyYDXQx9r3uBDiJ0KIpHB8YY2dfEMnu1l6tLadsZfv6GY7T4l4RyKEeBxYL4SYJYS43dr8g5TyOPAp0E0IMdY6Nqh/rxBiJvAbIYRrPZrpwDtCiA1ACvAA8DvraiNkGDv5hk52s95Da9sZe/mObrbzCillxN6ACcASoAswHjgCDHDZnwQ8BLzvss0eBB2xwK9QxSA/BS6ut/8+YKj1OAP4DJhs7KSXnXS1W6TYztirZdjOl5teXs17ooH1Uso9UspFwCvAf7vsLwPmAKVCiGeEEP8DdA6CjirgS6AvsAoYL4To4twppXxdSvm99bgIOA6kBkFHYxg7+YYudoPIsJ2xl+/oZDuviXRHkgCkCSHiAKSULwCZQojrrOcSOIuaqLoXKJJS7gq0CKlilnlSyjLgH0BHYJgQIhZqU/aEEKlCiBeBAcDaQOtoAmMn39DCbtZ7RYLtjL18Rxvb+US4h0Se3IBkl8e2evtWAte6PL8RWOny/M/WLSHYWly23wO8Dgx02dYF+Bb4A5AYJDulamSnRrWE205udPRpYl9I7dacHh1sB1wIZGhkr0b16GCvejpuAfrrYruA/V3hFtCM0S9F5Xu/C/zaZbsdiHUx9hKgs/U8G3jN+YMPxIVAi8352LpvBfwRuNn64vzE2p4WJDtdYtngXeBFl+22MNipKS1htVMjev8I7HHaJVx280BP2G3n8tn+GWjlsl2Ew17N6Am7veppHQhsRM3FDKynNSzftYD+feEW4MbgAvXjPB011LwMyEXFO39W79iu1vEzgbeBnwPzgDfDpCXF5fkvgBNAPnBZEO10NyoGfJX1xfsOuDRMdvJUS8js5E5rvefvA/8Gpjn/oUNlNx/1hNR21t9vA24CTgPXNaY/VPbyUk/Yvmv1dP0KuLuJ/UG3XVD/vnALaOJLcBnQw+X5Q6gFOVg/Wo8DRcAYoDUwCngHeDRMWo6gRi0C6A3sBp4IgZ36AlHW47bAP60fceeV2K9DaCdPtITMTs1odWp6ALgLNdo8z2X/DFTr0qDYzUc9BaG0XT1944BXgcHW8+tQ8w4xofie+ahHi++a9fwd4Err8cOo0VRKqL5rwb5FoQlCiPuBiUKIpcC7Usq5Qgi7ECJKSlmNWkC0wzq8LXAK6CmlPGFtWy6EWCWlrAmTlr5OLUKIvag4aJm/WprQtgSYLaXcZm0fjBoGR6G+mGeA/yQ0dvJGS0js1ITWxcA/pZSHhRAxqH/o24BM4EYhxBpUyuhp1MVDwO3mh54+obJd/f8BYBnQC3hdCNEG2IwKEx1FjdiD9j3zQ48O37WPpJSHgMNAWyHEp6gLglzgTiHEs5bWoH3XQkK4PZnlkaegQkfjgb+irjQGWfucV7h/A0a6ea2det4/jFqiwmCngda+rkC29TgJ9eUcHGI7eaolqHbyUOsQa9/T1r0zVLIdl7h5oO0WAD1Bt50bfa8BPYEs4AVqRwFpQDF1Y/6hsJc3esL9XXsNNSq/EVgI/M7FTgux5mqCZbtQ3XRJ/80F3pAqf/q/UJONvwCQUlZbKXudgO+FEB2FEHfBudo4NdL6FDTQUh1AHZ5qe8jStltKud96XIpKeUx10RYKO3mqJdh28kTrfda+y62r3BnAv1BzPGUuWgNtN3/1hMJ29fXtBR6TUh4G/ktKuR5ASlkMfAK0cdEXCnt5oyfc37V9wK+klLNRI5FoIUQ7qUYbK4BuLlqDYbuQEFJH4szddvN8N2pYipRyH/B/QKIQ4iprf2/U1caDwOcEYOGQTlr81JYghLiy3vFPAv2ArdaxPn85ddISYK0pQogRqIVfK6SUg6SUtwDtseow+atVNz1+6PsCSBZCXCmlPOty/FOoz3Z7IPTppidAWj8HMoQQo4HfoRZK/srSei2wONhaQ0GoRyTRrk9cjDcHKHf5sT6CyvjpY31AXVH/TF2Ay6WUs+q9PtK1+KutL4AQ4lIhxDLUsP9aKWVBC9MSSK0LgbGokhMzXF42xXmF2wL1+KPvO2o/2zFCiEWoz/YaKeXRFqonUFoXokLh61Gr1XegFiBOCuJnG1JC4kiEECOEEB+hCqP1FbVNWpyT/SdQ9XDutYZ4p1Cx9XjrA8oHRksp75VSHmkpWgKpzdq/HZgupbw1nHYKtJYgaU1E5eU7hEqksAG4XuG2FD0B0uf62e4Ffi6lvCUQn61ueoKgNdG6IaU8LqV8U0r5KynlwWBqDSWhqL7ZFjWZOBc1EfYg8DNQcw7WYfGomvtHgD8JIbJQTWeqrOM2Syn9Ll+gk5YAa6u0jtsrpdzSkrQEWWu1dVyNDFBpbt30BFif87M9IK0MvZamJ9haWywy+FkMFwEfWo8TgcmoBX29rW3Pogw/GDXf8Cxq2Po6Aa5uqZMWnbXppCXStOqmR3d9uulpKVpDfQuGsa8GnkDNH4Aq0bwT6GY9TwV+C8xCxQk/cO5zOUeg6j1po0VnbTppiTStuunRXZ9uelqK1nDfAml0Z03/JagFQYVYBchQud4vW49twGhUfZxmC/tFuhadtemkJdK06qZHd3266WkpWnW5BdL4w3FZ0o8qjLbCejwQVTdmkvW8DyotLjEYhtdJi87adNISaVp106O7Pt30tBStutz8KpEihLgV2A+sAb5HLaxy9kjehrV2AFW+YDbwshDiamAiqv5NNASmB7FOWnTWppOWSNOqmx7d9emmp6Vo1RGvHYkQQqAWSX0AOIBdqCJzD0opjwoh7FLKGiFEH1QBMqdx/2ZlPTyOWtR3l5TypD/iddKiszadtESaVt306K5PNz0tRav2eDnkc1Yo7Qn83Xocharz/0m9Y94Frrcet3c5R0wghlI6adFZm05aIk2rbnp016ebnpaiNRJuHo1IrMU2MwG7EGIuqklMDZyrP/UL4LAQYpyUcrH1slJgjxBiJvBTIcQlUsqDUkq/8ql10qKzNp20RJpW3fTork83PS1Fa0Thgeceh+rs9QZq2LcEVep6PzDM5bh7gUVOT47qL7wPeJlm2mB6etNJi87adNISaVp106O7Pt30tBStkXbzxPhjgFtcnr9uGfp24Htrmw0Va/wnkIOqaPkyVmnsAH4RtNGiszadtESaVt306K5PNz0tRWuk3TwxfgIQS2288D+A/7YebwAesB6fj2puFMwvgjZadNamk5ZI06qbHt316aanpWiNtFuztbaklOVSygpZ263rIlRLTYA7UFVxvwQ+RKXNNSixHCh00qKzNp20NIduWnXTo7s+3fQ0RSRpjTQ8Tv+18qkl0A61AAegBFVC4Dxgj1QtJZGWWw8WOmnRWZtOWppDN6266dFdn256miKStEYK3lT/daAW3RwDBlie+ynAIaVc5jR8iNBJi87adNLSHLpp1U1PfXTTp5uepogkrZGBN3EwVOkAB7AMuDOcMTmdtOisTSctkaZVNz2669NNT0vRGgk3YRnVI4QQHVF1Z16SUlZ447ACjU5a6qOTNp20NIduWnXTUx/d9OmmpykiSWsk4JUjMRgMBoOhPqHu2W4wGAyGFoZxJAaDwWDwC+NIDAaDweAXxpEYDAaDwS+MIzEYDAaDXxhHYjAYDAa/MI7EYDAYDH5hHInBYDAY/OL/AwepA45iFQWZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "win_size_days = 30\n",
    "predict_future_n_days = 10\n",
    "predict_df = train_and_valid(available_factor_df, y_arr, win_size_days, predict_future_n_days)\n",
    "predict_df = pd.DataFrame(predict_dic).T\n",
    "predict_df.plot()\n",
    "predict_df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
